1. Import packages and load data

import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import sklearn
import xgboost as xgb
from xgboost import XGBClassifier

# import logistic regression libraries
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, ConfusionMatrixDisplay, roc_curve, roc_auc_score, RocCurveDisplay, auc

# import classification tree libraries
from sklearn.tree import DecisionTreeClassifier, plot_tree

# import random forest libraries
from sklearn.ensemble import RandomForestClassifier
from sklearn.ensemble import BaggingClassifier

# import cross validation libraries
from sklearn.model_selection import GridSearchCV, KFold
from sklearn.preprocessing import StandardScaler

# load data
df = pd.read_csv(r"C:\Users\woowe\Downloads\hospital_readmissions.csv")
print(df.head())

       age  time_in_hospital  n_lab_procedures  n_procedures  n_medications  \
0  [70-80)                 8                72             1             18   
1  [70-80)                 3                34             2             13   
2  [50-60)                 5                45             0             18   
3  [70-80)                 2                36             0             12   
4  [60-70)                 1                42             0              7   

   n_outpatient  n_inpatient  n_emergency medical_specialty       diag_1  \
0             2            0            0           Missing  Circulatory   
1             0            0            0             Other        Other   
2             0            0            0           Missing  Circulatory   
3             1            0            0           Missing  Circulatory   
4             0            0            0  InternalMedicine        Other   

        diag_2       diag_3 glucose_test A1Ctest change diabetes_med  \
0  Respiratory        Other           no      no     no          yes   
1        Other        Other           no      no     no          yes   
2  Circulatory  Circulatory           no      no    yes          yes   
3        Other     Diabetes           no      no    yes          yes   
4  Circulatory  Respiratory           no      no     no          yes   

  readmitted  
0         no  
1         no  
2        yes  
3        yes  
4         no  

Exploratory Data Analysis

Data quantity

print("The raw dataset has {} rows and {} columns".format(df.shape[0], df.shape[1]))

The raw dataset has 25000 rows and 17 columns

Null values

# check null values
missing_counts = df.isnull().sum()
print("Count of Missing Values")
print(missing_counts)

Count of Missing Values
age                  0
time_in_hospital     0
n_lab_procedures     0
n_procedures         0
n_medications        0
n_outpatient         0
n_inpatient          0
n_emergency          0
medical_specialty    0
diag_1               0
diag_2               0
diag_3               0
glucose_test         0
A1Ctest              0
change               0
diabetes_med         0
readmitted           0
dtype: int64

Data types

# check data types
df.dtypes

age                  object
time_in_hospital      int64
n_lab_procedures      int64
n_procedures          int64
n_medications         int64
n_outpatient          int64
n_inpatient           int64
n_emergency           int64
medical_specialty    object
diag_1               object
diag_2               object
diag_3               object
glucose_test         object
A1Ctest              object
change               object
diabetes_med         object
readmitted           object
dtype: object

Univariate Distribution

# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns = df.select_dtypes(include=['int64']).columns.tolist()
cat_columns = df.select_dtypes(include=['object']).columns.tolist()

# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns, 1):
    plt.subplot(4, 5, i)
    sns.histplot(df[column], kde=False)
    plt.title(column)
plt.tight_layout()
plt.show()

# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns, 1):
    plt.subplot(1, len(cat_columns), i)
    sns.countplot(x=column, data=df)
    plt.title(column)
plt.tight_layout()
plt.show()

Summary table

# summary table of dataset
df.describe()

	time_in_hospital	n_lab_procedures	n_procedures	n_medications	n_outpatient	n_inpatient	n_emergency
count	25000.00000	25000.00000	25000.000000	25000.000000	25000.000000	25000.000000	25000.000000
mean	4.45332	43.24076	1.352360	16.252400	0.366400	0.615960	0.186600
std	3.00147	19.81862	1.715179	8.060532	1.195478	1.177951	0.885873
min	1.00000	1.00000	0.000000	1.000000	0.000000	0.000000	0.000000
25%	2.00000	31.00000	0.000000	11.000000	0.000000	0.000000	0.000000
50%	4.00000	44.00000	1.000000	15.000000	0.000000	0.000000	0.000000
75%	6.00000	57.00000	2.000000	20.000000	0.000000	1.000000	0.000000
max	14.00000	113.00000	6.000000	79.000000	33.000000	15.000000	64.000000

Check outliers

# check outliers
for column in df.select_dtypes(include=['number']).columns:
    plt.figure(figsize=(10, 6))  # Set figure size for each plot
    sns.boxplot(x=df[column])  # Create boxplot
    plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20})  # Set title
    plt.xlabel(column)  # Label x-axis
    plt.show()  # Display the plot

We noted that there were many outliers for n_lab_procedures, n_medications, n_outpatient, n_inpatient and n_emergency. However, since they are not data entry mistakes and are valid values, we decided to keep them.

2. Feature Engineering

Create interaction term 'severity'

• The new column severity reflects the interaction between time spent in the hospital and the combined number of lab and medical procedures.
• A higher value of severity could indicate a more serious condition, as it implies that patients with longer hospital stays and more procedures are likely to have greater health complexities.
• The term 32 * df['n_procedures'] adds a weighted contribution of the number of procedures, suggesting that each procedure has a significant impact on the overall severity.

# create interaction term 'severity'
df_new = df.copy()
df_new['severity'] = df_new['time_in_hospital'] * (df_new['n_lab_procedures'] + 32 * df_new['n_procedures'])

Transform existing variables

• For time_in_hospital which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_time_in_hospital.

• For n_procedures which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_n_procedures.

• For n_inpatient which is right-skewed, we apply a regular natural log transform: The transformed variable is named log_n_inpatient.

# Apply a log transform to existing variables
df_new["log_time_in_hospital"] = np.log1p(df_new["time_in_hospital"])
df_new["log_n_procedures"] = np.log1p(df_new["n_procedures"])
df_new["log_n_inpatient"] = np.log1p(df_new["n_inpatient"])

# Drop the original columns to prevent multicollinearity issues
df_new.drop(["time_in_hospital", "n_procedures", "n_inpatient"], axis = 1, inplace = True)

df_new.head()

	age	n_lab_procedures	n_medications	n_outpatient	n_emergency	medical_specialty	diag_1	diag_2	diag_3	glucose_test	A1Ctest	change	diabetes_med	readmitted	severity	log_time_in_hospital	log_n_procedures	log_n_inpatient
0	[70-80)	72	18	2	0	Missing	Circulatory	Respiratory	Other	no	no	no	yes	no	832	2.197225	0.693147	0.0
1	[70-80)	34	13	0	0	Other	Other	Other	Other	no	no	no	yes	no	294	1.386294	1.098612	0.0
2	[50-60)	45	18	0	0	Missing	Circulatory	Circulatory	Circulatory	no	no	yes	yes	yes	225	1.791759	0.000000	0.0
3	[70-80)	36	12	1	0	Missing	Circulatory	Other	Diabetes	no	no	yes	yes	yes	72	1.098612	0.000000	0.0
4	[60-70)	42	7	0	0	InternalMedicine	Other	Circulatory	Respiratory	no	no	no	yes	no	42	0.693147	0.000000	0.0

Exploratory Data Analysis (Processed Data)

Data types

# check data types
df_new.dtypes

age                      object
n_lab_procedures          int64
n_medications             int64
n_outpatient              int64
n_emergency               int64
medical_specialty        object
diag_1                   object
diag_2                   object
diag_3                   object
glucose_test             object
A1Ctest                  object
change                   object
diabetes_med             object
readmitted               object
severity                  int64
log_time_in_hospital    float64
log_n_procedures        float64
log_n_inpatient         float64
dtype: object

Univariate Distribution

# EDA on the entire raw dataset
# Define numeric and categorical columns for EDA
num_columns_new = df_new.select_dtypes(include=['int64', 'float64']).columns.tolist()
cat_columns_new = df_new.select_dtypes(include=['object']).columns.tolist()

# Plot numeric feature distributions
plt.figure(figsize=(30, 15))
for i, column in enumerate(num_columns_new, 1):
    plt.subplot(4, 5, i)
    sns.histplot(df_new[column], kde=False)
    plt.title(column)
plt.tight_layout()
plt.show()

# Plot categorical feature distributions
plt.figure(figsize=(30, 5))
for i, column in enumerate(cat_columns_new, 1):
    plt.subplot(1, len(cat_columns_new), i)
    sns.countplot(x=column, data=df)
    plt.title(column)
plt.tight_layout()
plt.show()

Summary table

# summary table of dataset
df_new.describe()

	n_lab_procedures	n_medications	n_outpatient	n_emergency	severity	log_time_in_hospital	log_n_procedures	log_n_inpatient
count	25000.00000	25000.000000	25000.000000	25000.000000	25000.000000	25000.000000	25000.000000	25000.000000
mean	43.24076	16.252400	0.366400	0.186600	434.342320	1.553614	0.626528	0.323864
std	19.81862	8.060532	1.195478	0.885873	519.559753	0.535475	0.656202	0.500605
min	1.00000	1.000000	0.000000	0.000000	1.000000	0.693147	0.000000	0.000000
25%	31.00000	11.000000	0.000000	0.000000	116.000000	1.098612	0.000000	0.000000
50%	44.00000	15.000000	0.000000	0.000000	250.000000	1.609438	0.693147	0.000000
75%	57.00000	20.000000	0.000000	0.000000	537.500000	1.945910	1.098612	0.693147
max	113.00000	79.000000	33.000000	64.000000	4004.000000	2.708050	1.945910	2.772589

Check outliers

# check outliers
for column in df_new.select_dtypes(include=['number']).columns:
    plt.figure(figsize=(10, 6))  # Set figure size for each plot
    sns.boxplot(x=df_new[column])  # Create boxplot
    plt.title(f"Boxplot of {column}", fontdict={'fontsize': 20})  # Set title
    plt.xlabel(column)  # Label x-axis
    plt.show()  # Display the plot

3. Data preprocessing

One-hot encoding

# one-hotting independent variables
df2 = pd.get_dummies(df_new, columns = ['diabetes_med'], drop_first = True, dtype = int)
df3 = pd.get_dummies(df2, columns = ['age'], drop_first = True, dtype = int)
df4 = pd.get_dummies(df3, columns = ['medical_specialty'], drop_first = True, dtype = int)
df5 = pd.get_dummies(df4, columns = ['diag_1'], drop_first = True, dtype = int)
df6 = pd.get_dummies(df5, columns = ['diag_2'], drop_first = True, dtype = int)
df7 = pd.get_dummies(df6, columns = ['diag_3'], drop_first = True, dtype = int)
df8 = pd.get_dummies(df7, columns = ['glucose_test'], drop_first = True, dtype = int)
df9 = pd.get_dummies(df8, columns = ['A1Ctest'], drop_first = True, dtype = int)
df10 = pd.get_dummies(df9, columns = ['change'], drop_first = True, dtype = int)

# one-hot readmission variable
df11 = pd.get_dummies(df10, columns = ['readmitted'], drop_first = True, dtype = int)
print(df11.head())

   n_lab_procedures  n_medications  n_outpatient  n_emergency  severity  \
0                72             18             2            0       832   
1                34             13             0            0       294   
2                45             18             0            0       225   
3                36             12             1            0        72   
4                42              7             0            0        42   

   log_time_in_hospital  log_n_procedures  log_n_inpatient  diabetes_med_yes  \
0              2.197225          0.693147              0.0                 1   
1              1.386294          1.098612              0.0                 1   
2              1.791759          0.000000              0.0                 1   
3              1.098612          0.000000              0.0                 1   
4              0.693147          0.000000              0.0                 1   

   age_[50-60)  ...  diag_3_Missing  diag_3_Musculoskeletal  diag_3_Other  \
0            0  ...               0                       0             1   
1            0  ...               0                       0             1   
2            1  ...               0                       0             0   
3            0  ...               0                       0             0   
4            0  ...               0                       0             0   

   diag_3_Respiratory  glucose_test_no  glucose_test_normal  A1Ctest_no  \
0                   0                1                    0           1   
1                   0                1                    0           1   
2                   0                1                    0           1   
3                   0                1                    0           1   
4                   1                1                    0           1   

   A1Ctest_normal  change_yes  readmitted_yes  
0               0           0               0  
1               0           0               0  
2               0           1               1  
3               0           1               1  
4               0           0               0  

[5 rows x 47 columns]

Train-test split

# define feature names for x and y datasets
# remove '[' for xgboost model to run successfully
y_name=['readmitted_yes']
x_name=['n_lab_procedures', 'n_medications', 'n_outpatient', 'n_emergency',
       'severity', 'log_time_in_hospital', 'log_n_procedures',
       'log_n_inpatient', 'diabetes_med_yes', 'age_50-60)', 'age_60-70)',
       'age_70-80)', 'age_80-90)', 'age_90-100)',
       'medical_specialty_Emergency/Trauma',
       'medical_specialty_Family/GeneralPractice',
       'medical_specialty_InternalMedicine', 'medical_specialty_Missing',
       'medical_specialty_Other', 'medical_specialty_Surgery',
       'diag_1_Diabetes', 'diag_1_Digestive', 'diag_1_Injury',
       'diag_1_Missing', 'diag_1_Musculoskeletal', 'diag_1_Other',
       'diag_1_Respiratory', 'diag_2_Diabetes', 'diag_2_Digestive',
       'diag_2_Injury', 'diag_2_Missing', 'diag_2_Musculoskeletal',
       'diag_2_Other', 'diag_2_Respiratory', 'diag_3_Diabetes',
       'diag_3_Digestive', 'diag_3_Injury', 'diag_3_Missing',
       'diag_3_Musculoskeletal', 'diag_3_Other', 'diag_3_Respiratory',
       'glucose_test_no', 'glucose_test_normal', 'A1Ctest_no',
       'A1Ctest_normal', 'change_yes']

# define features (X) and target (y)
df_X = df11.iloc[:, :-1] # drop 'readmitted_yes'
df_y = df11.iloc[:, -1:] # only store 'readmitted_yes'

# split data points (rows) into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(df_X, df_y, test_size = 0.2, random_state = 42)

# Scale the features
scaler = StandardScaler()
X_train_scaled = scaler.fit_transform(X_train)
X_test_scaled = scaler.transform(X_test)

# Convert scaled features back to DataFrame
df_X_train_scaled = pd.DataFrame(X_train_scaled, columns=x_name).reset_index(drop=True)
df_X_test_scaled = pd.DataFrame(X_test_scaled, columns=x_name).reset_index(drop=True)

df_y_train = pd.DataFrame(y_train, columns=y_name).reset_index(drop=True)
df_y_test = pd.DataFrame(y_test, columns=y_name).reset_index(drop=True)

df_train = pd.concat([df_X_train_scaled, df_y_train], axis = 1)
df_test = pd.concat([df_X_test_scaled, df_y_test], axis = 1)

To check any null values in final datasets

df_train.isnull().sum()

n_lab_procedures                            0
n_medications                               0
n_outpatient                                0
n_emergency                                 0
severity                                    0
log_time_in_hospital                        0
log_n_procedures                            0
log_n_inpatient                             0
diabetes_med_yes                            0
age_50-60)                                  0
age_60-70)                                  0
age_70-80)                                  0
age_80-90)                                  0
age_90-100)                                 0
medical_specialty_Emergency/Trauma          0
medical_specialty_Family/GeneralPractice    0
medical_specialty_InternalMedicine          0
medical_specialty_Missing                   0
medical_specialty_Other                     0
medical_specialty_Surgery                   0
diag_1_Diabetes                             0
diag_1_Digestive                            0
diag_1_Injury                               0
diag_1_Missing                              0
diag_1_Musculoskeletal                      0
diag_1_Other                                0
diag_1_Respiratory                          0
diag_2_Diabetes                             0
diag_2_Digestive                            0
diag_2_Injury                               0
diag_2_Missing                              0
diag_2_Musculoskeletal                      0
diag_2_Other                                0
diag_2_Respiratory                          0
diag_3_Diabetes                             0
diag_3_Digestive                            0
diag_3_Injury                               0
diag_3_Missing                              0
diag_3_Musculoskeletal                      0
diag_3_Other                                0
diag_3_Respiratory                          0
glucose_test_no                             0
glucose_test_normal                         0
A1Ctest_no                                  0
A1Ctest_normal                              0
change_yes                                  0
readmitted_yes                              0
dtype: int64

df_test.isnull().sum()

n_lab_procedures                            0
n_medications                               0
n_outpatient                                0
n_emergency                                 0
severity                                    0
log_time_in_hospital                        0
log_n_procedures                            0
log_n_inpatient                             0
diabetes_med_yes                            0
age_50-60)                                  0
age_60-70)                                  0
age_70-80)                                  0
age_80-90)                                  0
age_90-100)                                 0
medical_specialty_Emergency/Trauma          0
medical_specialty_Family/GeneralPractice    0
medical_specialty_InternalMedicine          0
medical_specialty_Missing                   0
medical_specialty_Other                     0
medical_specialty_Surgery                   0
diag_1_Diabetes                             0
diag_1_Digestive                            0
diag_1_Injury                               0
diag_1_Missing                              0
diag_1_Musculoskeletal                      0
diag_1_Other                                0
diag_1_Respiratory                          0
diag_2_Diabetes                             0
diag_2_Digestive                            0
diag_2_Injury                               0
diag_2_Missing                              0
diag_2_Musculoskeletal                      0
diag_2_Other                                0
diag_2_Respiratory                          0
diag_3_Diabetes                             0
diag_3_Digestive                            0
diag_3_Injury                               0
diag_3_Missing                              0
diag_3_Musculoskeletal                      0
diag_3_Other                                0
diag_3_Respiratory                          0
glucose_test_no                             0
glucose_test_normal                         0
A1Ctest_no                                  0
A1Ctest_normal                              0
change_yes                                  0
readmitted_yes                              0
dtype: int64

4. Model Training

K-fold cross validation is used since there is no class imbalance issue for our target variable.

# K-fold CV splitter
kf10 = KFold(n_splits = 10, shuffle = True, random_state = 42)

# Manually generate CV folds
def get_CV_folds(df, y, X, cv):
    train, val = [], []
    for train_index, val_index in cv.split(df[X], df[y]):
        train.append(df.loc[train_index])
        val.append(df.loc[val_index])
    return train, val

# Report GridSearchCV results
def report_GridSearchCV_results(grid):
    print("- Best combination of hyperparams:\n", grid.best_params_, "\n")
    print("- Best mean_test_score:\n", grid.best_score_, "\n")
    
    scores = []
    for i in range(grid.n_splits_):
        scores.append(grid.cv_results_['split{}_test_score'.format(i)][grid.best_index_])
    print("- Score by fold for best estimator:\n", scores, "\n")
    
    # View top 10 hyperparameter combinations by mean_test_score (mean AUC on validation set)
    print("- Top 10 hyperparameter combinations by mean_test_score:")
    display(pd.DataFrame(grid.cv_results_)[["rank_test_score", "mean_test_score"] 
                                            + ["param_" + param for param in grid.param_grid]]\
              .sort_values(by = "mean_test_score", ascending = False)\
              .set_index("rank_test_score").head(10))
    
    return None

# Compare training dataset performance vs validation dataset performance
def compare_performance(grid):  
    # retrieve training and validation scores
    train_scores=grid.cv_results_['mean_train_score']
    val_scores=grid.cv_results_['mean_test_score']

    # limit to 10 rows
    train_scores_limited=train_scores[:10]
    val_scores_limited=val_scores[:10]
    
    # create dataframe to store scores
    all_scores=pd.DataFrame({
    "train_AUC": train_scores_limited, 
    "val_AUC": val_scores_limited
    }, index=range(1,11))

    mean_scores=pd.DataFrame({
        "train_AUC": [train_scores_limited.mean()],
        "val_AUC": [val_scores_limited.mean()]
    }, index=["Mean"])

    all_scores_combined=pd.concat([all_scores, mean_scores])
    
    return all_scores_combined

# Plot histogram of SD of P(Readmitted) when training set varies
def plot_probability_std(estimator, df, y, X, cv, model_name):
    train, val = get_CV_folds(df, y, X, cv)
    test = val[-1]
    prob = pd.DataFrame() 
    
    for i in range(cv.n_splits - 1):
        train = val[i]
        estimator = estimator.fit(train[X], train[y])
        prob["Fold {}".format(i+1)] = [pred[1] for pred in estimator.predict_proba(test[X])]
        
    prob_std = prob.apply(lambda x: x.std(ddof=0), axis = 1)
    
    plt.figure(figsize = (8, 5))
    plt.hist(prob_std, rwidth = 0.6, bins = np.arange(0, 0.1, 0.01))
    plt.title("{} || SD of probability predictions when training set varies".format(model_name), fontsize = 14)
    plt.ylabel("Count of test observations", fontsize = 12)
    plt.xlabel("SD of P(Readmitted)", fontsize = 12)
    plt.show()
    
    return None

# Plot average feature importance across CV folds
def plot_avg_feature_importance(tree, df, y, X, cv, model_name):
    
    train, val = get_CV_folds(df, y, X, cv)
    impt = pd.DataFrame()
    
    for i in range(cv.n_splits):
        df_train = train[i]
        tree = tree.fit(df_train[X], df_train[y])
        impt[str(i)] = tree.feature_importances_
        
    ft = list(zip(X, impt.mean(axis = 1)))
    ft.sort(key = lambda x: x[1])
    plt.figure(figsize = (8, 10))
    features, importances = [x[0] for x in ft], [x[1] for x in ft]
    plt.barh(features, importances)
    plt.title("{} || Avg. feature importance across CV folds".format(model_name), fontsize = 14)
    plt.show()    

    return None

# evaluate model on test set
def evaluate_model(best_model, X_test_scaled, y_test): 
    """
    Parameters: 
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test data
    - y_test: True labels for test set
    """
    # predict probabilities and labels
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]
    y_pred=best_model.predict(X_test_scaled)

    # metrics
    test_auc=roc_auc_score(y_test, y_prob)
    accuracy=accuracy_score(y_test, y_pred)
    conf_matrix=confusion_matrix(y_test, y_pred)
    classification_rep=classification_report(y_test, y_pred)

    # print metrics
    print(f"Test AUC: {test_auc:.2f}")
    print(f'Accuracy: {accuracy:.2f}')
    print('Confusion Matrix:'); print(conf_matrix)
    disp = ConfusionMatrixDisplay(confusion_matrix=conf_matrix)
    disp.plot()
    plt.show()
    print('Classification Report:')
    print(classification_rep)

# Plot ROC curve on test set
def plot_roc_curve(best_model, X_test_scaled, y_test):
    """
    Parameters:
    - best_model: The best estimator from grid search
    - X_test_scaled: Scaled test features
    - y_test: True labels for the test set
    """
    # predict probabilities
    y_prob=best_model.predict_proba(X_test_scaled)[:,1]

    # compute roc curve
    fpr, tpr, thresholds= roc_curve(y_test, y_prob)
    roc_auc=auc(fpr, tpr)

    # plot roc curve
    plt.figure(figsize=(8, 6))
    plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'AUC = {roc_auc:.2f}')
    plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
    plt.xlim([0.0, 1.0])
    plt.ylim([0.0, 1.05])
    plt.xlabel('False Positive Rate')
    plt.ylabel('True Positive Rate')
    plt.title('Receiver Operating Characteristic (ROC) Curve')
    plt.legend(loc='lower right')
    plt.show()

Classification Tree

Pre-Pruning

# Initialize model
classificationtree = DecisionTreeClassifier(random_state=42)

# Define the hyperparameter grid
clf_param_grid = {
    'max_depth': [None, 2, 3, 4, 5],
    'min_samples_split': [2, 5, 10],
    'min_samples_leaf': [1, 2, 4],
    'max_leaf_nodes': [None, 5, 10, 15]
}

# Create a GridSearchCV object
grid_search_clf = GridSearchCV(estimator=classificationtree, param_grid=clf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_clf.fit(df_train[x_name], df_train[y_name])

Fitting 10 folds for each of 180 candidates, totalling 1800 fits
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.528) total time=   0.5s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=1.000, test=0.533) total time=   0.4s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.993, test=0.531) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.993, test=0.550) total time=   0.3s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.556) total time=   0.3s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.524) total time=   0.3s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.537) total time=   0.4s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.529) total time=   0.4s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.527) total time=   0.4s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.546) total time=   0.3s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.993, test=0.536) total time=   0.4s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.994, test=0.537) total time=   0.3s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.964, test=0.549) total time=   0.4s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.962, test=0.562) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.963, test=0.536) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.963, test=0.545) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.963, test=0.542) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.964, test=0.538) total time=   0.2s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.965, test=0.550) total time=   0.3s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.961, test=0.549) total time=   0.3s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.962, test=0.542) total time=   0.7s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.546) total time=   0.7s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.989, test=0.566) total time=   0.9s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.989, test=0.554) total time=   0.5s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.529) total time=   0.5s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.545) total time=   0.4s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.548) total time=   0.4s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.529) total time=   0.3s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.552) total time=   0.4s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.548) total time=   0.3s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.988, test=0.544) total time=   0.4s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.551) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.557) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.551) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.532) total time=   0.1s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.539) total time=   0.1s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.553) total time=   0.1s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.985, test=0.554) total time=   0.2s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.555) total time=   0.2s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.984, test=0.541) total time=   0.2s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.953, test=0.551) total time=   0.1s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.950, test=0.563) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.953, test=0.554) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.950, test=0.544) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.951, test=0.553) total time=   0.3s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.951, test=0.552) total time=   0.3s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.954, test=0.547) total time=   0.3s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.954, test=0.557) total time=   0.4s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.949, test=0.556) total time=   0.3s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.951, test=0.551) total time=   0.3s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.551) total time=   0.3s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.550) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.940, test=0.576) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.561) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.553) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.940, test=0.554) total time=   0.2s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.551) total time=   0.2s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.936, test=0.570) total time=   0.2s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.938, test=0.550) total time=   0.2s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.551) total time=   0.2s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.550) total time=   0.1s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.940, test=0.576) total time=   0.3s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.561) total time=   0.1s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.553) total time=   0.1s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.940, test=0.554) total time=   0.1s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.551) total time=   0.1s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.936, test=0.570) total time=   0.2s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.938, test=0.550) total time=   0.2s
[CV 1/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.560) total time=   0.2s
[CV 2/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.930, test=0.574) total time=   0.2s
[CV 4/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.545) total time=   0.2s
[CV 5/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.927, test=0.563) total time=   0.2s
[CV 6/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.564) total time=   0.2s
[CV 7/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.930, test=0.560) total time=   0.2s
[CV 8/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.929, test=0.551) total time=   0.2s
[CV 9/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.926, test=0.567) total time=   0.1s
[CV 10/10] END max_depth=None, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.928, test=0.557) total time=   0.1s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.644) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.640, test=0.617) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.651) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.614) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.629) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.643, test=0.643) total time=   0.0s
[CV 2/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.638) total time=   0.0s
[CV 3/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.636) total time=   0.0s
[CV 4/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.647, test=0.621) total time=   0.0s
[CV 5/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.647) total time=   0.0s
[CV 6/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.654) total time=   0.0s
[CV 7/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 8/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.645, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.645, test=0.635) total time=   0.0s
[CV 10/10] END max_depth=None, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.631) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 2/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.620) total time=   0.0s
[CV 3/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.616) total time=   0.0s
[CV 4/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.595) total time=   0.0s
[CV 5/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.614, test=0.628) total time=   0.0s
[CV 6/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.621) total time=   0.0s
[CV 7/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 8/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.618, test=0.591) total time=   0.0s
[CV 9/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.615, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=2, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.616) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.1s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.635) total time=   0.0s
[CV 2/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.636) total time=   0.0s
[CV 3/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 4/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.630, test=0.610) total time=   0.0s
[CV 5/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.627, test=0.639) total time=   0.0s
[CV 6/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.623, test=0.630) total time=   0.0s
[CV 7/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.632) total time=   0.0s
[CV 8/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.601) total time=   0.0s
[CV 9/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=3, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.628) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.623) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.638) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.604) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.638) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.616) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.640) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.645) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.632, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.628) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.633, test=0.632) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.640) total time=   0.1s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.639) total time=   0.1s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.640) total time=   0.0s
[CV 2/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.622) total time=   0.0s
[CV 3/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.635) total time=   0.0s
[CV 4/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.615) total time=   0.0s
[CV 5/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.641) total time=   0.0s
[CV 6/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.639) total time=   0.0s
[CV 8/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.603) total time=   0.0s
[CV 9/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.619) total time=   0.0s
[CV 10/10] END max_depth=4, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.634, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.645, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.645, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.634) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.645, test=0.613) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.640) total time=   0.1s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=1, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.647, test=0.616) total time=   0.1s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.647, test=0.611) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.647, test=0.616) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=2, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.642, test=0.640) total time=   0.1s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.641) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.635) total time=   0.1s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.647, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.647, test=0.617) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=2;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.646, test=0.643) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.641) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.647, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.647, test=0.617) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=5;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.643, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.626) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.642, test=0.640) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.645, test=0.612) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.646, test=0.643) total time=   0.1s
[CV 6/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.641) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.635) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.647, test=0.610) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.647, test=0.617) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=None, min_samples_leaf=4, min_samples_split=10;, score=(train=0.644, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=1, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=2, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=2;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=5;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.625, test=0.632) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.621) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.623) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.628, test=0.607) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.624, test=0.638) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.623) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.616, test=0.620) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.629, test=0.599) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.625) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=5, min_samples_leaf=4, min_samples_split=10;, score=(train=0.626, test=0.624) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.1s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=1, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.642) total time=   0.1s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.1s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.1s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.1s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.618) total time=   0.1s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=2, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=2;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=5;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.642) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.634) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.642) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.648) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.637) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.608) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.636, test=0.633) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=10, min_samples_leaf=4, min_samples_split=10;, score=(train=0.635, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=2;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=5;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=1, min_samples_split=10;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.638, test=0.642) total time=   0.1s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=2;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=5;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.645) total time=   0.1s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.643, test=0.605) total time=   0.1s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=2, min_samples_split=10;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=2;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=5;, score=(train=0.640, test=0.633) total time=   0.0s
[CV 1/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.645) total time=   0.0s
[CV 2/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.631) total time=   0.0s
[CV 3/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.637, test=0.634) total time=   0.0s
[CV 4/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.618) total time=   0.0s
[CV 5/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.640, test=0.644) total time=   0.0s
[CV 6/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.638, test=0.642) total time=   0.0s
[CV 7/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.639, test=0.636) total time=   0.0s
[CV 8/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.643, test=0.605) total time=   0.0s
[CV 9/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.641, test=0.621) total time=   0.0s
[CV 10/10] END max_depth=5, max_leaf_nodes=15, min_samples_leaf=4, min_samples_split=10;, score=(train=0.640, test=0.633) total time=   0.0s

GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'max_depth': [None, 2, 3, 4, 5],
                         'max_leaf_nodes': [None, 5, 10, 15],
                         'min_samples_leaf': [1, 2, 4],
                         'min_samples_split': [2, 5, 10]},
             return_train_score=True, scoring='roc_auc', verbose=4)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Model Performance

report_GridSearchCV_results(grid_search_clf)

- Best combination of hyperparams:
 {'max_depth': None, 'max_leaf_nodes': 15, 'min_samples_leaf': 1, 'min_samples_split': 2} 

- Best mean_test_score:
 0.6348561664387089 

- Score by fold for best estimator:
 [0.642826028833473, 0.6381912555990646, 0.6356443108525275, 0.6211688074352548, 0.6465267259472497, 0.6542109592126485, 0.6326661628391814, 0.6113999785603581, 0.6350141122190772, 0.6309133228882546] 

- Top 10 hyperparameter combinations by mean_test_score:

	mean_test_score	param_max_depth	param_min_samples_split	param_min_samples_leaf	param_max_leaf_nodes
rank_test_score
1	0.634856	None	2	2	15
1	0.634856	None	5	1	15
1	0.634856	None	10	4	15
1	0.634856	None	5	4	15
1	0.634856	None	2	4	15
1	0.634856	None	10	2	15
1	0.634856	None	5	2	15
1	0.634856	None	10	1	15
1	0.634856	None	2	1	15
10	0.633061	None	5	2	10

compare_performance(grid_search_clf)

	train_AUC	val_AUC
1	1.000000	0.533032
2	0.993662	0.537205
3	0.963196	0.546496
4	0.988201	0.546305
5	0.984342	0.547125
6	0.951628	0.552855
7	0.938195	0.555678
8	0.938195	0.555678
9	0.928294	0.559897
10	0.622964	0.621077
Mean	0.930868	0.555535

best_model_clf=grid_search_clf.best_estimator_

plot_probability_std(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")

plot_avg_feature_importance(best_model_clf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")

evaluate_model(best_model_clf, df_X_test_scaled, df_y_test)

Test AUC: 0.63
Accuracy: 0.60
Confusion Matrix:
[[1721  937]
 [1074 1268]]

Classification Report:
              precision    recall  f1-score   support

           0       0.62      0.65      0.63      2658
           1       0.58      0.54      0.56      2342

    accuracy                           0.60      5000
   macro avg       0.60      0.59      0.59      5000
weighted avg       0.60      0.60      0.60      5000

plot_roc_curve(best_model_clf, df_X_test_scaled, df_y_test)

# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_clf, filled=True, feature_names=x_name, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()

Post-pruning

# Get effective alphas for pruning
path = classificationtree.cost_complexity_pruning_path(X_train_scaled, y_train)
ccp_alphas=path.ccp_alphas
impurities=path.impurities

# Define the hyperparameter grid
post_prune_param_grid = {
    'ccp_alpha': ccp_alphas
}

# Create a GridSearchCV object
grid_search_post_prune = GridSearchCV(estimator=classificationtree, param_grid=post_prune_param_grid, scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_post_prune.fit(df_train[x_name], df_train[y_name])

Fitting 10 folds for each of 2446 candidates, totalling 24460 fits
[CV 1/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.528) total time=   0.4s
[CV 5/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.545) total time=   0.4s
[CV 6/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 7/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=2.666666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=2.8571428571428574e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=2.9166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.552) total time=   0.3s
[CV 4/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=2.9629629629629627e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.0555555555555554e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.095238095238096e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.111111111111112e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 10/10] END ccp_alpha=3.125e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 1/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.137254901960785e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.1578947368421065e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 10/10] END ccp_alpha=3.166666666666666e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.174603174603176e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.533) total time=   0.3s
[CV 9/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.1818181818181834e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.194444444444446e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.545) total time=   0.4s
[CV 6/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.4s
[CV 7/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.3s
[CV 8/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 10/10] END ccp_alpha=3.199999999999999e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.535) total time=   0.3s
[CV 2/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.528) total time=   0.3s
[CV 5/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.545) total time=   0.3s
[CV 6/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.209876543209875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 7/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.214285714285714e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.535) total time=   0.2s
[CV 2/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.2183908045976986e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.222222222222221e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.243243243243246e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.518) total time=   0.2s
[CV 8/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.245614035087716e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.249999999999997e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 6/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.253968253968252e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.269230769230774e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.2941176470588205e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.2s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.2s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.2s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 6/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.517) total time=   0.2s
[CV 7/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=3.333333333333333e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 1/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.6363636363636364e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 1/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.714285714285716e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.0s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.0s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.75e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.8888888888888884e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.950617283950617e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.3s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=3.999999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 2/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=4.156862745098037e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.5s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.5s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.4s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.5s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.5s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.5s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.6s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.9s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   1.0s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.6s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.6s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.7s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.6s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.5s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.3s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.6s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.4s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.4s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.5s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.3s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.3s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.1s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.530) total time=   0.3s
[CV 9/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.524) total time=   0.2s
[CV 10/10] END ccp_alpha=4.1666666666666665e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.214285714285714e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.230769230769231e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.241486068111453e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.526) total time=   0.4s
[CV 5/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.515) total time=   0.5s
[CV 7/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.516) total time=   0.5s
[CV 8/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 9/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.525) total time=   0.5s
[CV 10/10] END ccp_alpha=4.285714285714286e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 2/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.539) total time=   0.5s
[CV 3/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 5/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.515) total time=   0.4s
[CV 7/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.6s
[CV 9/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.4s
[CV 10/10] END ccp_alpha=4.333333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.4s
[CV 1/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 5/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.345238095238097e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.36853002070393e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.4s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.4s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.4s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.3s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.3s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.3s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.3s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.3s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.3s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.4s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.3s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 9/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.375e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.0s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4117647058823526e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.423076923076923e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4272727272727236e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.428571428571428e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 10/10] END ccp_alpha=4.439102564102557e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.444444444444444e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.45925925925926e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.4999999999999996e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.500000000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5029239766081865e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.523809523809524e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.541666666666669e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.545454545454546e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.562500000000001e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.565217391304347e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.566666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 2/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.571428571428572e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.5833333333333334e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.2s
[CV 2/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.599999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.60486272986273e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.615384615384613e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.526) total time=   0.2s
[CV 10/10] END ccp_alpha=4.6179401993355466e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.526) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6296296296296294e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.642857142857144e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.655172413793102e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 2/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.666666666666668e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.670454545454554e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.533) total time=   0.2s
[CV 2/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.543) total time=   0.2s
[CV 3/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6774193548387085e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.6875e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.514) total time=   0.1s
[CV 8/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.696969696969695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7058823529411774e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.710884353741497e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.714285714285712e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7222222222222235e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.729729729729729e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.73684210526316e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 9/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749034749034749e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.749999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 9/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.528) total time=   0.1s
[CV 10/10] END ccp_alpha=4.756097560975609e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7619047619047634e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.767441860465116e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.772727272727275e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.780701754385964e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.782608695652175e-05;, score=(train=1.000, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.791666666666667e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.7916666666666695e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.799999999999999e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8076923076923104e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 1/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.814814814814813e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 10/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.530) total time=   0.2s
[CV 10/10] END ccp_alpha=4.821428571428571e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.827586206896548e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.545) total time=   0.2s
[CV 3/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.519) total time=   0.2s
[CV 7/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.532) total time=   0.2s
[CV 9/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8315508021390405e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.838709677419352e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8412698412698446e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.84375e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8484848484848454e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.532) total time=   0.1s
[CV 9/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.857142857142857e-05;, score=(train=1.000, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.546) total time=   0.1s
[CV 3/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=4.861111111111113e-05;, score=(train=1.000, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.861111111111113e-05;, score=(train=0.999, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=4.861111111111113e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.861111111111113e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.546) total time=   0.1s
[CV 3/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=1.000, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=0.999, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.8684210526315744e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.533) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9282296650717744e-05;, score=(train=1.000, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.983164983164985e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.992260061919513e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.2s
[CV 2/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=4.9999999999999996e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.3s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.2s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.2s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.2s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 10/10] END ccp_alpha=5e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.0000000000000016e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.539) total time=   0.2s
[CV 9/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.000000000000002e-05;, score=(train=0.999, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 6/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.999, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.0666666666666654e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 6/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.075581395348838e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 6/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.079365079365081e-05;, score=(train=0.999, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.0946969696969684e-05;, score=(train=0.998, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.111111111111112e-05;, score=(train=0.998, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.998, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.519) total time=   0.1s
[CV 7/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.162037037037036e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.544) total time=   0.2s
[CV 3/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.208333333333334e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.516) total time=   0.2s
[CV 7/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.227272727272729e-05;, score=(train=0.997, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.995, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.997, test=0.548) total time=   0.2s
[CV 4/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.517) total time=   0.1s
[CV 7/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.256410256410258e-05;, score=(train=0.996, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.271739130434781e-05;, score=(train=0.996, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.548) total time=   0.2s
[CV 4/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.518) total time=   0.1s
[CV 7/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.2777777777777784e-05;, score=(train=0.996, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333332e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.2s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.2s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.2s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.546) total time=   0.1s
[CV 4/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.995, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=5.333333333333333e-05;, score=(train=0.996, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.3571428571428575e-05;, score=(train=0.995, test=0.526) total time=   0.1s
[CV 1/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.994, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.3846153846153853e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.394759598754754e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.4019607843137246e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.536) total time=   0.2s
[CV 2/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=5.4210526315789485e-05;, score=(train=0.995, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.444444444444447e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.454545454545455e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.995, test=0.514) total time=   0.1s
[CV 7/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.470085470085466e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.4751131221719436e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.511363636363635e-05;, score=(train=0.995, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.499) total time=   0.1s
[CV 8/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.546558704453439e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.499) total time=   0.1s
[CV 8/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.5468749999999996e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.994, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555553e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.498) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555555e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.555555555555556e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.565934065934065e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.5752561071710005e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.592105263157893e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.6084656084656106e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.499) total time=   0.1s
[CV 8/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.994, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.6249999999999985e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.627705627705628e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.641025641025641e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.6418918918918925e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.666666666666666e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.678571428571431e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.68825910931174e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.7142857142857115e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285714e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285715e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.714285714285717e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=5.7397959183673446e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=5.74175824175824e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.7590439276485733e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.544) total time=   0.2s
[CV 4/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.995, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.777777777777779e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.785256410256413e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.993, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.8026315789473675e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.993, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.81818181818182e-05;, score=(train=0.995, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333333e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.833333333333334e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.846153846153847e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.88235294117647e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.883333333333335e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.534) total time=   0.1s
[CV 9/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.892857142857144e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.925925925925925e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9340659340659366e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.939440993788818e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.952380952380954e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.963407661520868e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.964912280701754e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.993, test=0.532) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9722222222222206e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.2s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=5.9999999999999995e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.000000000000001e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0000000000000015e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.000000000000002e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.001602564102561e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.011363636363636e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.022727272727272e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0277777777777776e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0317460317460316e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.994, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.052631578947368e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.060606060606061e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.543) total time=   0.3s
[CV 4/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.0606060606060625e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.066911464708283e-05;, score=(train=0.994, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.543) total time=   0.2s
[CV 4/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.086956521739129e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.095238095238091e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.103896103896104e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.111111111111111e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.994, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.111111111111112e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.133333333333332e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.134453781512605e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.153846153846154e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 10/10] END ccp_alpha=6.16339622641509e-05;, score=(train=0.994, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.17283950617284e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.190476190476192e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.535) total time=   0.1s
[CV 2/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.19285714285714e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.205128205128205e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.214285714285714e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.222222222222222e-05;, score=(train=0.994, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.236559139784945e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.25e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.25e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.255411255411255e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.256410256410255e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.262626262626264e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.992, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.267806267806266e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.273310023310027e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.282051282051283e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.302083333333333e-05;, score=(train=0.993, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.32019115890083e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.321022727272726e-05;, score=(train=0.993, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.333333333333333e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.333333333333335e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.533) total time=   0.1s
[CV 2/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.992, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.991, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.349206349206349e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.991, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.992, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.388888888888888e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.400000000000009e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.403162055335971e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.410256410256413e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.410256410256414e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.413994169096208e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.2s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.991, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.428571428571426e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.991, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.991, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.428571428571429e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.993, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.434389140271492e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.535) total time=   0.2s
[CV 9/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.992, test=0.535) total time=   0.1s
[CV 9/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.991, test=0.534) total time=   0.2s
[CV 10/10] END ccp_alpha=6.464646464646463e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.534) total time=   0.1s
[CV 2/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.991, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.991, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.991, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.477272727272727e-05;, score=(train=0.993, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.523809523809524e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.510) total time=   0.2s
[CV 7/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.992, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 10/10] END ccp_alpha=6.534090909090907e-05;, score=(train=0.993, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.540880503144647e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.536) total time=   0.2s
[CV 2/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.510) total time=   0.1s
[CV 7/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.547619047619045e-05;, score=(train=0.992, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.555183946488298e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.564102564102567e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.566666666666667e-05;, score=(train=0.992, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.536) total time=   0.1s
[CV 2/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.575221238938051e-05;, score=(train=0.992, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.992, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.990, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.992, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.991, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.590909090909089e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.642414403778043e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.992, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.648351648351654e-05;, score=(train=0.992, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.3s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.3s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.3s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.3s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.3s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.3s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.3s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.2s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.2s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.990, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666666e-05;, score=(train=0.991, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.991, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.989, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.666666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.2s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.66666666666667e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.671390065460151e-05;, score=(train=0.990, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.989, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.673878412124349e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.674603174603171e-05;, score=(train=0.990, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.687499999999999e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.696832579185519e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 9/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.706349206349208e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.706539074960129e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.531) total time=   0.2s
[CV 6/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.716180371352785e-05;, score=(train=0.990, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.724137931034483e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.727272727272729e-05;, score=(train=0.990, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.740196078431374e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.749999999999999e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.750000000000001e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.531) total time=   0.1s
[CV 6/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.757936507936508e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.774193548387098e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.511) total time=   0.1s
[CV 7/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.785714285714287e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.2s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.2s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.805555555555554e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.812499999999999e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.812865497076024e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.537) total time=   0.2s
[CV 2/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.818181818181818e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.989, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 10/10] END ccp_alpha=6.837121212121216e-05;, score=(train=0.990, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 2/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.862745098039216e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.891891891891892e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.990, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.987, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=6.898672512483255e-05;, score=(train=0.989, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.990, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.988, test=0.536) total time=   0.1s
[CV 9/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.909090909090908e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.923076923076922e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 2/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.9377990430622e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 2/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.990, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.512) total time=   0.1s
[CV 7/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.990, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.938387635756057e-05;, score=(train=0.989, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.944444444444446e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.953748006379585e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.965944272445824e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.969696969696971e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.984126984126984e-05;, score=(train=0.989, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.988, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.991758241758243e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.988, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=6.993006993006993e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.2s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.000000000000001e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.004310344827582e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.005555555555554e-05;, score=(train=0.989, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.986, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.012987012987011e-05;, score=(train=0.989, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.533) total time=   0.1s
[CV 3/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.015915119363394e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.034632034632043e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.989, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.986, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.040816326530612e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.041666666666663e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.988, test=0.513) total time=   0.1s
[CV 7/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.054972303144679e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.989, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.986, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.988, test=0.513) total time=   0.2s
[CV 7/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.06172839506173e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.989, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.07446808510638e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.987, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.986, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.083333333333332e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.986, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.085714285714285e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.986, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.09090909090909e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.986, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.09359605911329e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.987, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.094017094017095e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.986, test=0.537) total time=   0.2s
[CV 10/10] END ccp_alpha=7.101449275362311e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.102272727272724e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.102272727272727e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.108843537414966e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111108e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.989, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111109e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.540) total time=   0.2s
[CV 2/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111112e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.986, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.989, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.111111111111115e-05;, score=(train=0.988, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.987, test=0.535) total time=   0.2s
[CV 3/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.118675595238095e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.119986518368715e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.124579124579122e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.987, test=0.535) total time=   0.1s
[CV 3/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.128823073945025e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.985, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.989, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.12962962962963e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.986, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.134387351778656e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857141e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.1s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.3s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.987, test=0.534) total time=   0.2s
[CV 3/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.985, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.142857142857143e-05;, score=(train=0.988, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.986, test=0.539) total time=   0.2s
[CV 9/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.166666666666668e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.988, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.987, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.989, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.17105263157895e-05;, score=(train=0.987, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.986, test=0.539) total time=   0.1s
[CV 9/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.986, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.179487179487172e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.988, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.986, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.187499999999999e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.988, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.987, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.988, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.986, test=0.537) total time=   0.2s
[CV 10/10] END ccp_alpha=7.192982456140351e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202380952380956e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.985, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.202797202797201e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.20588235294118e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.539) total time=   0.2s
[CV 2/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.988, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.218045112781948e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.986, test=0.532) total time=   0.1s
[CV 3/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.985, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.987, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.988, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.220867208672095e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.986, test=0.532) total time=   0.2s
[CV 3/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.988, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.986, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.243107769423562e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.987, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.985, test=0.537) total time=   0.2s
[CV 10/10] END ccp_alpha=7.254901960784314e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.987, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.985, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.263224763224764e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.266483516483516e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727271e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.515) total time=   0.2s
[CV 7/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.272727272727273e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.985, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.282170542635656e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.988, test=0.539) total time=   0.1s
[CV 2/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.984, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.285714285714287e-05;, score=(train=0.987, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.986, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.984, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.515) total time=   0.1s
[CV 7/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.985, test=0.538) total time=   0.2s
[CV 9/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.306698268470113e-05;, score=(train=0.987, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.322388632872504e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.347222222222228e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.988, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.350000000000005e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.985, test=0.538) total time=   0.1s
[CV 9/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.35294117647059e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.357142857142854e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.361111111111111e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.986, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.361111111111113e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.540) total time=   0.1s
[CV 2/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.988, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.363636363636364e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.366946778711487e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.985, test=0.531) total time=   0.1s
[CV 3/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.984, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.369304923751385e-05;, score=(train=0.987, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.985, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.384615384615384e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.985, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.987, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.983, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.387057387057391e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.985, test=0.530) total time=   0.1s
[CV 3/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.987, test=0.536) total time=   0.1s
[CV 4/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.988, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.389583333333334e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.541) total time=   0.1s
[CV 2/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.985, test=0.530) total time=   0.2s
[CV 3/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.536) total time=   0.1s
[CV 4/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.983, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.986, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.516) total time=   0.1s
[CV 7/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.987, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.985, test=0.537) total time=   0.1s
[CV 9/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.984, test=0.536) total time=   0.1s
[CV 10/10] END ccp_alpha=7.394510828976839e-05;, score=(train=0.986, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.407407407407407e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.525) total time=   0.2s
[CV 6/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.407834101382489e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.410256410256413e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.980, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.412587412587413e-05;, score=(train=0.981, test=0.524) total time=   0.2s
[CV 1/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.42063492063492e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 1/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.420849420849417e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.424242424242424e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.982, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.427018633540368e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.982, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.980, test=0.536) total time=   0.1s
[CV 3/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.981, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.446153846153838e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.982, test=0.525) total time=   0.1s
[CV 6/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.981, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.457671957671959e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.982, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.981, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.979, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.464470284237736e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.982, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.982, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.473812709030101e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.982, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.982, test=0.525) total time=   0.2s
[CV 6/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.979, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.979, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.485380116959071e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.983, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.979, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.493540051679589e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.3s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.3s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.2s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.5e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.5e-05;, score=(train=0.981, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.5e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.5e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.5e-05;, score=(train=0.979, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.5e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.982, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.982, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.981, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.979, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.500000000000001e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 1/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.983, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.979, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.53076496674058e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.983, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.535612535612538e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.982, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.980, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.542) total time=   0.1s
[CV 2/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 3/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.981, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.982, test=0.500) total time=   0.1s
[CV 8/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.544642857142854e-05;, score=(train=0.980, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.2s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757574e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.575757575757576e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.579787234042554e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.981, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.978, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.582750582750582e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.981, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.978, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.982, test=0.501) total time=   0.1s
[CV 8/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.589743589743589e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.603519668737063e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.981, test=0.526) total time=   0.2s
[CV 6/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.604166666666664e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.980, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.978, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.605263157894735e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.981, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.981, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.981, test=0.526) total time=   0.1s
[CV 6/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.606060606060604e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.617241379310352e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 3/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.978, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.980, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.617554858934175e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047618e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.2s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.2s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047621e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.543) total time=   0.1s
[CV 2/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.527) total time=   0.1s
[CV 6/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.619047619047623e-05;, score=(train=0.979, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.544) total time=   0.1s
[CV 2/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.978, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.977, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.528) total time=   0.1s
[CV 6/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.982, test=0.502) total time=   0.2s
[CV 8/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.977, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.977, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.634575569358173e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.544) total time=   0.1s
[CV 2/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.978, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.977, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.670329670329668e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.544) total time=   0.1s
[CV 2/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.674863387978142e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.977, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.977, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.681992337164749e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.686274509803923e-05;, score=(train=0.979, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=7.6875e-05;, score=(train=0.980, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.6875e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.6875e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.6875e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.6875e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.6875e-05;, score=(train=0.980, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.6875e-05;, score=(train=0.982, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.6875e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.6875e-05;, score=(train=0.977, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.6875e-05;, score=(train=0.978, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.546) total time=   0.2s
[CV 2/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.980, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.977, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=7.692307692307693e-05;, score=(train=0.978, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.977, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.976, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.977, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.7037037037037e-05;, score=(train=0.978, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.978, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.977, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.979, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.981, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.718172718172718e-05;, score=(train=0.978, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.977, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.979, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.727888480456971e-05;, score=(train=0.978, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.7511961722488e-05;, score=(train=0.978, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.981, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.752100840336135e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.2s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.2s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.980, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.979, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.976, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=7.756410256410256e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.979, test=0.537) total time=   0.2s
[CV 4/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.776497695852528e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.979, test=0.546) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.979, test=0.537) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.979, test=0.529) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.981, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777774e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.2s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.2s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.2s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.2s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.547) total time=   0.1s
[CV 2/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.538) total time=   0.1s
[CV 4/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.979, test=0.530) total time=   0.1s
[CV 6/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.981, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 10/10] END ccp_alpha=7.777777777777777e-05;, score=(train=0.977, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.979, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=7.781385281385283e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.979, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.980, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.79238287832826e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.980, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 10/10] END ccp_alpha=7.797619047619043e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.980, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.800269905533063e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.977, test=0.538) total time=   0.2s
[CV 3/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.976, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.805668016194333e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.8125e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.8125e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.8125e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.8125e-05;, score=(train=0.980, test=0.504) total time=   0.2s
[CV 8/10] END ccp_alpha=7.8125e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.8125e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.8125e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.978, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.81289667428281e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.978, test=0.549) total time=   0.2s
[CV 2/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.977, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.813852813852811e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.81385281385282e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.814327485380116e-05;, score=(train=0.977, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.978, test=0.549) total time=   0.2s
[CV 2/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.817385866166376e-05;, score=(train=0.977, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.977, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.977, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.823749415614775e-05;, score=(train=0.977, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.978, test=0.533) total time=   0.2s
[CV 6/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.975, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.828947368421053e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.975, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.975, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.830882352941177e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.978, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.975, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.831807780320368e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.975, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.980, test=0.504) total time=   0.1s
[CV 8/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.837037037037041e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.978, test=0.533) total time=   0.2s
[CV 6/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.980, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.847826086956521e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.980, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.854166666666667e-05;, score=(train=0.976, test=0.519) total time=   0.1s
[CV 1/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.978, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.977, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.980, test=0.502) total time=   0.1s
[CV 8/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.85714285714286e-05;, score=(train=0.976, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.977, test=0.532) total time=   0.2s
[CV 6/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.875787815126051e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.977, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.979, test=0.503) total time=   0.2s
[CV 8/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.876984126984128e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.977, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.885057471264362e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.977, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.977, test=0.532) total time=   0.1s
[CV 6/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.889610389610392e-05;, score=(train=0.976, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.975, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.977, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.894736842105265e-05;, score=(train=0.976, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.978, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.977, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.975, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.977, test=0.533) total time=   0.1s
[CV 6/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.899644215433687e-05;, score=(train=0.976, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.976, test=0.538) total time=   0.1s
[CV 3/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.977, test=0.543) total time=   0.1s
[CV 4/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.903225806451608e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.974, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.907608695652176e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.976, test=0.534) total time=   0.1s
[CV 6/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.912087912087919e-05;, score=(train=0.975, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.535) total time=   0.2s
[CV 6/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.976, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.977, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.916666666666668e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.977, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.974, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.535) total time=   0.1s
[CV 6/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.976, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.979, test=0.503) total time=   0.1s
[CV 8/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.973, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.92857142857143e-05;, score=(train=0.975, test=0.517) total time=   0.1s
[CV 1/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.976, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.936507936507937e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.976, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.944444444444444e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.976, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.973, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.976, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.946969696969694e-05;, score=(train=0.974, test=0.515) total time=   0.1s
[CV 1/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.976, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.964015151515146e-05;, score=(train=0.974, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.973, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 10/10] END ccp_alpha=7.973484848484848e-05;, score=(train=0.974, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.974, test=0.538) total time=   0.2s
[CV 10/10] END ccp_alpha=7.976190476190474e-05;, score=(train=0.974, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.976, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999998e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.2s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.2s
[CV 1/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=7.999999999999999e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.536) total time=   0.1s
[CV 6/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.978, test=0.505) total time=   0.2s
[CV 8/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.976, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.539) total time=   0.1s
[CV 4/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.536) total time=   0.2s
[CV 6/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.978, test=0.505) total time=   0.1s
[CV 8/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 10/10] END ccp_alpha=8.000000000000002e-05;, score=(train=0.973, test=0.516) total time=   0.1s
[CV 1/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.972, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=8.020535714285717e-05;, score=(train=0.973, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.972, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=8.021390374331547e-05;, score=(train=0.973, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.972, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 10/10] END ccp_alpha=8.021390374331552e-05;, score=(train=0.973, test=0.517) total time=   0.2s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.2s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.975, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.978, test=0.506) total time=   0.2s
[CV 8/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.047619047619048e-05;, score=(train=0.973, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.048433048433049e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.540) total time=   0.2s
[CV 3/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.971, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.975, test=0.537) total time=   0.2s
[CV 6/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.974, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.971, test=0.540) total time=   0.2s
[CV 9/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.055555555555554e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.971, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.062678062678062e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.971, test=0.540) total time=   0.1s
[CV 9/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.064713064713068e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 5/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 9/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.067226890756305e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.974, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.068639612499261e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.975, test=0.537) total time=   0.1s
[CV 6/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.974, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.069264069264068e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.975, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.975, test=0.537) total time=   0.2s
[CV 6/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.974, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.978, test=0.506) total time=   0.1s
[CV 8/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.07692307692308e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.974, test=0.538) total time=   0.1s
[CV 6/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.086153846153848e-05;, score=(train=0.972, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.974, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.972, test=0.542) total time=   0.2s
[CV 10/10] END ccp_alpha=8.095744680851063e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.11594202898551e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.977, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.115942028985514e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.973, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.115942028985519e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.1203007518797e-05;, score=(train=0.972, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.125e-05;, score=(train=0.975, test=0.551) total time=   0.2s
[CV 2/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.125e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.125e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.125e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.125e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.125e-05;, score=(train=0.975, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.125e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.125e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.125e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.125e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.125e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.125e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.132716049382716e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.977, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.138461538461544e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.972, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.973, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.1394764329547e-05;, score=(train=0.971, test=0.518) total time=   0.1s
[CV 1/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.972, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.977, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.144607843137255e-05;, score=(train=0.971, test=0.518) total time=   0.2s
[CV 1/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.976, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.973, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.976, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.969, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.148148148148148e-05;, score=(train=0.971, test=0.519) total time=   0.2s
[CV 1/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.976, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.153757568809727e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.166666666666663e-05;, score=(train=0.971, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.973, test=0.539) total time=   0.2s
[CV 3/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.974, test=0.539) total time=   0.2s
[CV 6/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.969, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.166666666666668e-05;, score=(train=0.971, test=0.520) total time=   0.2s
[CV 1/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.973, test=0.539) total time=   0.1s
[CV 3/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.974, test=0.539) total time=   0.1s
[CV 6/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.976, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.167420814479632e-05;, score=(train=0.971, test=0.520) total time=   0.1s
[CV 1/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 3/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.971, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.174182139699375e-05;, score=(train=0.971, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.2s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.2s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.181818181818183e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.976, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.186024057609536e-05;, score=(train=0.970, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.973, test=0.540) total time=   0.1s
[CV 6/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.975, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.971, test=0.541) total time=   0.2s
[CV 10/10] END ccp_alpha=8.187830687830687e-05;, score=(train=0.970, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.974, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 5/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.972, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.971, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.19345661450924e-05;, score=(train=0.969, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.974, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.973, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.970, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.973, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.968, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.202682563338308e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.542) total time=   0.2s
[CV 10/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.205128205128207e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.975, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.20512820512821e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.973, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.217804363124559e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.971, test=0.521) total time=   0.2s
[CV 7/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.221906116642953e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.972, test=0.540) total time=   0.2s
[CV 4/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.970, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 6/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.971, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.227225672877863e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 3/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.972, test=0.540) total time=   0.1s
[CV 4/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.970, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 6/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.971, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.975, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.968, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.970, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.229755178907724e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.969, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.967, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.248050421963464e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.25e-05;, score=(train=0.973, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.25e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.25e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.25e-05;, score=(train=0.969, test=0.523) total time=   0.1s
[CV 5/10] END ccp_alpha=8.25e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.25e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.25e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.25e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.25e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.25e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.968, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 4/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.971, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.26719576719577e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.541) total time=   0.2s
[CV 4/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.971, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.975, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.269230769230771e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.969, test=0.524) total time=   0.2s
[CV 5/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.968, test=0.521) total time=   0.2s
[CV 1/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 4/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 4/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.975, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.970, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.27205882352941e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.970, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.969, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.28947368421053e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.972, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.969, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.292364914202262e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.972, test=0.541) total time=   0.1s
[CV 3/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.974, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.969, test=0.541) total time=   0.1s
[CV 10/10] END ccp_alpha=8.300653594771242e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.969, test=0.524) total time=   0.1s
[CV 5/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.970, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.969, test=0.542) total time=   0.2s
[CV 10/10] END ccp_alpha=8.309485956544776e-05;, score=(train=0.968, test=0.521) total time=   0.1s
[CV 1/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.971, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.969, test=0.525) total time=   0.2s
[CV 5/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.321428571428569e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.970, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.968, test=0.525) total time=   0.1s
[CV 5/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.325123152709354e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.972, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.971, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.970, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.968, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.971, test=0.542) total time=   0.2s
[CV 6/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.969, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.974, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.969, test=0.542) total time=   0.1s
[CV 10/10] END ccp_alpha=8.331456456456454e-05;, score=(train=0.968, test=0.522) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.972, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.971, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.970, test=0.544) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.968, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.971, test=0.542) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.969, test=0.521) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.974, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.966, test=0.541) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.969, test=0.543) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333332e-05;, score=(train=0.968, test=0.522) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.3s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.2s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.2s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.2s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.545) total time=   0.1s
[CV 4/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.965, test=0.520) total time=   0.2s
[CV 7/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.961, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.333333333333333e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.966, test=0.542) total time=   0.1s
[CV 3/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.963, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.964, test=0.520) total time=   0.1s
[CV 7/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.961, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.339920948616598e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.969, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.961, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.964, test=0.546) total time=   0.2s
[CV 10/10] END ccp_alpha=8.348343685300188e-05;, score=(train=0.963, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.968, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.363095238095238e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.964, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.365583270345174e-05;, score=(train=0.962, test=0.525) total time=   0.2s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.2s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.965, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.522) total time=   0.1s
[CV 7/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.968, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.964, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.366013071895421e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 1/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.543) total time=   0.1s
[CV 3/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.963, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.964, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.968, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.963, test=0.546) total time=   0.1s
[CV 10/10] END ccp_alpha=8.390151515151514e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.964, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.968, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.402777777777781e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 4/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.406432748538016e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.967, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.967, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.964, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.960, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.409090909090911e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.964, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.963, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.964, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.963, test=0.523) total time=   0.1s
[CV 7/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.968, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.960, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.417391304347829e-05;, score=(train=0.962, test=0.527) total time=   0.1s
[CV 1/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.964, test=0.544) total time=   0.1s
[CV 3/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.964, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.963, test=0.548) total time=   0.2s
[CV 6/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.962, test=0.544) total time=   0.2s
[CV 10/10] END ccp_alpha=8.420333967448746e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.966, test=0.548) total time=   0.2s
[CV 2/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.433085346149255e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.545) total time=   0.2s
[CV 3/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.963, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.433333333333334e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.966, test=0.548) total time=   0.1s
[CV 2/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.548) total time=   0.1s
[CV 6/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.967, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.434210526315792e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.4375e-05;, score=(train=0.966, test=0.549) total time=   0.1s
[CV 2/10] END ccp_alpha=8.4375e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.4375e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.4375e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.4375e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.4375e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.4375e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.4375e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.4375e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.4375e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.966, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.444444444444442e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.963, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.959, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.962, test=0.545) total time=   0.2s
[CV 10/10] END ccp_alpha=8.444444444444443e-05;, score=(train=0.961, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.545) total time=   0.2s
[CV 10/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.451563691838288e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.967, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.452380952380956e-05;, score=(train=0.960, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.456659619450327e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.545) total time=   0.2s
[CV 3/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 5/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 6/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.967, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.959, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.960, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.966, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.963, test=0.547) total time=   0.2s
[CV 4/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 6/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.959, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.461538461538461e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.966, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.963, test=0.545) total time=   0.1s
[CV 3/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.963, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 5/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 6/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.967, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.959, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.962, test=0.545) total time=   0.1s
[CV 10/10] END ccp_alpha=8.464285714285708e-05;, score=(train=0.960, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 6/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.966, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.477564102564108e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 6/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.962, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.966, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.477822580645164e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.962, test=0.547) total time=   0.1s
[CV 4/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.961, test=0.552) total time=   0.2s
[CV 6/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.962, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.966, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.487107797452624e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.965, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 6/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.507326007326006e-05;, score=(train=0.959, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.964, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.552) total time=   0.1s
[CV 6/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.959, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.964, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.962, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.552) total time=   0.1s
[CV 6/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.961, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.957, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.960, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.522727272727273e-05;, score=(train=0.959, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.964, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.962, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.960, test=0.552) total time=   0.1s
[CV 6/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.961, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.965, test=0.507) total time=   0.1s
[CV 8/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.956, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.526315789473684e-05;, score=(train=0.959, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.964, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.961, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.961, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.960, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.956, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.54365079365079e-05;, score=(train=0.959, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.964, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.961, test=0.548) total time=   0.3s
[CV 3/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.961, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.960, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.959, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.960, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.964, test=0.508) total time=   0.3s
[CV 8/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.956, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.960, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.544871794871796e-05;, score=(train=0.959, test=0.529) total time=   0.3s
[CV 1/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.964, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.961, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.961, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.960, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.960, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.956, test=0.545) total time=   0.2s
[CV 9/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.547008547008542e-05;, score=(train=0.959, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.960, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.56739700113194e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.960, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.964, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.960, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.569444444444443e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.964, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428571e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.960, test=0.548) total time=   0.2s
[CV 3/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428573e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.956, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.959, test=0.547) total time=   0.2s
[CV 10/10] END ccp_alpha=8.571428571428574e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.960, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.956, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 10/10] END ccp_alpha=8.571428571428578e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.955, test=0.544) total time=   0.3s
[CV 9/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.959, test=0.548) total time=   0.2s
[CV 10/10] END ccp_alpha=8.595238095238098e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.955, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.959, test=0.548) total time=   0.2s
[CV 10/10] END ccp_alpha=8.5952380952381e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.961, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.527) total time=   0.2s
[CV 5/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.597826086956522e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.961, test=0.550) total time=   0.3s
[CV 4/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.601190476190481e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.963, test=0.550) total time=   0.2s
[CV 2/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 5/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.602941176470585e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.548) total time=   0.2s
[CV 10/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.1s
[CV 5/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 6/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.602941176470591e-05;, score=(train=0.958, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.962, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.958, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.955, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.611111111111112e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.962, test=0.550) total time=   0.1s
[CV 2/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.959, test=0.547) total time=   0.1s
[CV 3/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.961, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.958, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 6/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.963, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.955, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.958, test=0.548) total time=   0.1s
[CV 10/10] END ccp_alpha=8.615228131357165e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.962, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.959, test=0.548) total time=   0.1s
[CV 3/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.960, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.958, test=0.527) total time=   0.1s
[CV 5/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.959, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.962, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.954, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.958, test=0.549) total time=   0.1s
[CV 10/10] END ccp_alpha=8.625500828958276e-05;, score=(train=0.956, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.962, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 3/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.960, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.962, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.954, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.958, test=0.549) total time=   0.1s
[CV 10/10] END ccp_alpha=8.63334683556734e-05;, score=(train=0.956, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.962, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 3/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.960, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.957, test=0.529) total time=   0.1s
[CV 5/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.959, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.962, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.954, test=0.542) total time=   0.2s
[CV 9/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.958, test=0.549) total time=   0.2s
[CV 10/10] END ccp_alpha=8.641344956413448e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 3/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.962, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.953, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 10/10] END ccp_alpha=8.643790849673203e-05;, score=(train=0.954, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.961, test=0.551) total time=   0.2s
[CV 2/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 3/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.957, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.962, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.953, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.957, test=0.551) total time=   0.2s
[CV 10/10] END ccp_alpha=8.648376623376615e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.961, test=0.551) total time=   0.1s
[CV 2/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 3/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.959, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.962, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.953, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 10/10] END ccp_alpha=8.651574543342836e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.956, test=0.530) total time=   0.2s
[CV 5/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.958, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.962, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 10/10] END ccp_alpha=8.653846153846156e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.662878787878798e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.959, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.961, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.957, test=0.552) total time=   0.2s
[CV 10/10] END ccp_alpha=8.663319838056681e-05;, score=(train=0.954, test=0.529) total time=   0.2s
[CV 1/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.666666666666664e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.957, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.958, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.961, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.666666666666668e-05;, score=(train=0.954, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.957, test=0.551) total time=   0.1s
[CV 3/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.959, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.956, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.956, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.958, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.961, test=0.507) total time=   0.2s
[CV 8/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.953, test=0.542) total time=   0.1s
[CV 9/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 10/10] END ccp_alpha=8.678571428571432e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.957, test=0.552) total time=   0.2s
[CV 3/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.956, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.957, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.955, test=0.553) total time=   0.1s
[CV 10/10] END ccp_alpha=8.695738749502192e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.961, test=0.552) total time=   0.1s
[CV 2/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.957, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.956, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.957, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.955, test=0.553) total time=   0.2s
[CV 10/10] END ccp_alpha=8.700000000000001e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.960, test=0.553) total time=   0.1s
[CV 2/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.959, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 10/10] END ccp_alpha=8.709295250062965e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.960, test=0.553) total time=   0.1s
[CV 2/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.957, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.711541565778852e-05;, score=(train=0.953, test=0.529) total time=   0.1s
[CV 1/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.960, test=0.553) total time=   0.1s
[CV 2/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.956, test=0.552) total time=   0.2s
[CV 3/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.956, test=0.531) total time=   0.1s
[CV 5/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.957, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.952, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.712660028449519e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.530) total time=   0.2s
[CV 5/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272723e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.2s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.1s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 3/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.959, test=0.549) total time=   0.1s
[CV 4/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.530) total time=   0.2s
[CV 5/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.957, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.727272727272728e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.955, test=0.553) total time=   0.2s
[CV 3/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.956, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.733277591973243e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.956, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.956, test=0.525) total time=   0.2s
[CV 7/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.739495798319327e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.743330930064884e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.2s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.75e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.75e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.75e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.75e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.75e-05;, score=(train=0.955, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.960, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.955, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.952, test=0.543) total time=   0.1s
[CV 9/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.750000000000001e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.958, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.955, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.960, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.952, test=0.544) total time=   0.2s
[CV 9/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.955, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.753501400560226e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.532) total time=   0.3s
[CV 5/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.952, test=0.544) total time=   0.3s
[CV 9/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.75757575757575e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.959, test=0.554) total time=   0.1s
[CV 2/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.956, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.760122779519333e-05;, score=(train=0.952, test=0.528) total time=   0.2s
[CV 1/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.959, test=0.554) total time=   0.2s
[CV 2/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.958, test=0.550) total time=   0.2s
[CV 4/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.956, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.960, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.952, test=0.544) total time=   0.1s
[CV 9/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.761904761904761e-05;, score=(train=0.952, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.959, test=0.555) total time=   0.2s
[CV 2/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.957, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.956, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.951, test=0.545) total time=   0.1s
[CV 9/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.778334725703142e-05;, score=(train=0.951, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.959, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.957, test=0.550) total time=   0.1s
[CV 4/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.953, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.954, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.955, test=0.524) total time=   0.2s
[CV 7/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.960, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.951, test=0.546) total time=   0.1s
[CV 9/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.954, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.78452380952381e-05;, score=(train=0.951, test=0.528) total time=   0.1s
[CV 1/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.955, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.959, test=0.510) total time=   0.1s
[CV 8/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.951, test=0.547) total time=   0.1s
[CV 9/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.953, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.800000000000002e-05;, score=(train=0.950, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.956, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.955, test=0.524) total time=   0.1s
[CV 7/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.959, test=0.510) total time=   0.2s
[CV 8/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.803705692803436e-05;, score=(train=0.950, test=0.531) total time=   0.2s
[CV 1/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.954, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.959, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.953, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.821428571428573e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.954, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.959, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.82352941176471e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.958, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.952, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.952, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.954, test=0.525) total time=   0.1s
[CV 7/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.958, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.953, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.839285714285714e-05;, score=(train=0.949, test=0.530) total time=   0.1s
[CV 1/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.952, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.952, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.954, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.957, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.952, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.854166666666667e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.952, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.956, test=0.551) total time=   0.2s
[CV 4/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.951, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.953, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.957, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.952, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.85989010989011e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.957, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.952, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.956, test=0.551) total time=   0.1s
[CV 4/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.951, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.953, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.957, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.950, test=0.548) total time=   0.1s
[CV 9/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.951, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.864468864468861e-05;, score=(train=0.949, test=0.531) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.951, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888888e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.951, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.953, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888889e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.951, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.952, test=0.508) total time=   0.1s
[CV 8/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.555) total time=   0.2s
[CV 10/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.951, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.948, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.952, test=0.508) total time=   0.2s
[CV 8/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.945, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=8.888888888888892e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.953, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.947, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.948, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.952, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.902593295382668e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.536) total time=   0.3s
[CV 5/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.947, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.945, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=8.909090909090911e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.952, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.947, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.947, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.947, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.950, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.952, test=0.509) total time=   0.2s
[CV 8/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.945, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=8.912599340230925e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.946, test=0.555) total time=   0.3s
[CV 3/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.950, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.949, test=0.526) total time=   0.2s
[CV 7/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.549) total time=   0.1s
[CV 9/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.946, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.950, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.947, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.949, test=0.526) total time=   0.1s
[CV 7/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.952, test=0.509) total time=   0.1s
[CV 8/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.549) total time=   0.2s
[CV 9/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.945, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=8.918617614269792e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.951, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.947, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.946, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.949, test=0.527) total time=   0.2s
[CV 7/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.952, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=8.940448823207444e-05;, score=(train=0.943, test=0.536) total time=   0.1s
[CV 1/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.949, test=0.527) total time=   0.1s
[CV 7/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.946, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.949, test=0.527) total time=   0.2s
[CV 7/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.951, test=0.511) total time=   0.2s
[CV 8/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.941798941798952e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.950, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.946, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.947, test=0.528) total time=   0.2s
[CV 7/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.951, test=0.511) total time=   0.1s
[CV 8/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.944, test=0.551) total time=   0.1s
[CV 9/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.944444444444423e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.1s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.2s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.2s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.2s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.946, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.951, test=0.512) total time=   0.1s
[CV 8/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.951048951048949e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.945, test=0.554) total time=   0.2s
[CV 3/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.950, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.946, test=0.535) total time=   0.2s
[CV 5/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.946, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.951, test=0.512) total time=   0.2s
[CV 8/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.943, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.961988304093567e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 1/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.950, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.945, test=0.554) total time=   0.1s
[CV 3/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.950, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.946, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.947, test=0.528) total time=   0.1s
[CV 7/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.951, test=0.512) total time=   0.1s
[CV 8/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.943, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.967032967032966e-05;, score=(train=0.943, test=0.535) total time=   0.2s
[CV 1/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.950, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.948, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.947, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.943, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.99425287356322e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999997e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=8.999999999999999e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.1s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.945, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.946, test=0.529) total time=   0.2s
[CV 7/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.000000000000002e-05;, score=(train=0.942, test=0.537) total time=   0.2s
[CV 1/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.946, test=0.530) total time=   0.1s
[CV 7/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.513) total time=   0.2s
[CV 8/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.948, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.946, test=0.530) total time=   0.1s
[CV 7/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.552) total time=   0.1s
[CV 9/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.944, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.019607843137258e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.949, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.948, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.945, test=0.536) total time=   0.1s
[CV 5/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.945, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.946, test=0.530) total time=   0.1s
[CV 7/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.949, test=0.513) total time=   0.1s
[CV 8/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.942, test=0.552) total time=   0.2s
[CV 9/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.944, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.02003910068426e-05;, score=(train=0.942, test=0.537) total time=   0.1s
[CV 1/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.945, test=0.536) total time=   0.2s
[CV 5/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.945, test=0.533) total time=   0.1s
[CV 7/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.949, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.942, test=0.553) total time=   0.2s
[CV 9/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.043133462282404e-05;, score=(train=0.941, test=0.538) total time=   0.2s
[CV 1/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.948, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.948, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.945, test=0.533) total time=   0.1s
[CV 7/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.949, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.941, test=0.555) total time=   0.2s
[CV 9/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.047619047619048e-05;, score=(train=0.941, test=0.538) total time=   0.1s
[CV 1/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.942, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.945, test=0.533) total time=   0.2s
[CV 7/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.949, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.941, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.048387096774192e-05;, score=(train=0.941, test=0.538) total time=   0.2s
[CV 1/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.948, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.945, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.949, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.941, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=9.049615547682561e-05;, score=(train=0.941, test=0.538) total time=   0.1s
[CV 1/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.942, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.944, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.945, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.949, test=0.515) total time=   0.2s
[CV 8/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.941, test=0.555) total time=   0.2s
[CV 9/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.051851851851853e-05;, score=(train=0.941, test=0.538) total time=   0.1s
[CV 1/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.948, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.942, test=0.555) total time=   0.2s
[CV 3/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.948, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.944, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.945, test=0.534) total time=   0.2s
[CV 7/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.948, test=0.515) total time=   0.1s
[CV 8/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.941, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.054131054131052e-05;, score=(train=0.941, test=0.538) total time=   0.2s
[CV 1/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.947, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.941, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.947, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.943, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.948, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.940, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.074074074074073e-05;, score=(train=0.941, test=0.539) total time=   0.1s
[CV 1/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.947, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.941, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.947, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.943, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.948, test=0.516) total time=   0.2s
[CV 8/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.940, test=0.554) total time=   0.2s
[CV 9/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.074074074074079e-05;, score=(train=0.941, test=0.539) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.3s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.944, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909092e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.946, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.943, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.943, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.944, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.090909090909093e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.944, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.100609756097562e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.946, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.944, test=0.535) total time=   0.2s
[CV 7/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.103352565309113e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.940, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.946, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.943, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 9/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.107291077155253e-05;, score=(train=0.939, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.945, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.942, test=0.534) total time=   0.2s
[CV 7/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.11764705882353e-05;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.945, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.941, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.120000000000001e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.945, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.945, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 5/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.126559714795019e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.945, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.944, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.942, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.128787878787878e-05;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.945, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.944, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.942, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.130884917175243e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.941, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.137529137529118e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.13764958875929e-05;, score=(train=0.938, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.941, test=0.557) total time=   0.2s
[CV 10/10] END ccp_alpha=9.138730139058074e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142050524542627e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142732706814987e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.14285714285713e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.942, test=0.534) total time=   0.2s
[CV 7/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142857142857142e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.142857142857145e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.941, test=0.533) total time=   0.2s
[CV 5/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.942, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.946, test=0.517) total time=   0.2s
[CV 8/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.143101111412305e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.944, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.941, test=0.533) total time=   0.1s
[CV 5/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.942, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.942, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.144118269492412e-05;, score=(train=0.938, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.944, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.941, test=0.534) total time=   0.1s
[CV 7/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.941, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.163636363636364e-05;, score=(train=0.937, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 9/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.939, test=0.557) total time=   0.2s
[CV 3/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.541) total time=   0.2s
[CV 1/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.939, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.944, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.946, test=0.517) total time=   0.1s
[CV 8/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 9/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.166666666666667e-05;, score=(train=0.937, test=0.541) total time=   0.1s
[CV 1/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.943, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.941, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.942, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.946, test=0.518) total time=   0.1s
[CV 8/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.937, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.940, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.175434303890424e-05;, score=(train=0.937, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.944, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.938, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.943, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.940, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.941, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.941, test=0.535) total time=   0.1s
[CV 7/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.945, test=0.520) total time=   0.1s
[CV 8/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.936, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.940, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.20261437908497e-05;, score=(train=0.937, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.942, test=0.554) total time=   0.2s
[CV 4/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.940, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.940, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.944, test=0.524) total time=   0.1s
[CV 8/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.2239010989011e-05;, score=(train=0.936, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.940, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.944, test=0.524) total time=   0.1s
[CV 8/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.939, test=0.556) total time=   0.1s
[CV 10/10] END ccp_alpha=9.224999999999999e-05;, score=(train=0.936, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.942, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.940, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.943, test=0.525) total time=   0.2s
[CV 8/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.230769230769226e-05;, score=(train=0.935, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.942, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.937, test=0.556) total time=   0.2s
[CV 3/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.942, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.939, test=0.532) total time=   0.2s
[CV 5/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.939, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.939, test=0.536) total time=   0.2s
[CV 7/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.943, test=0.525) total time=   0.2s
[CV 8/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.934, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.938, test=0.555) total time=   0.1s
[CV 10/10] END ccp_alpha=9.239766081871343e-05;, score=(train=0.935, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.942, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.937, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.941, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.939, test=0.532) total time=   0.1s
[CV 5/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.939, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.939, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.943, test=0.525) total time=   0.2s
[CV 8/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.934, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.938, test=0.556) total time=   0.2s
[CV 10/10] END ccp_alpha=9.246794871794873e-05;, score=(train=0.935, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.941, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.935, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.940, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.938, test=0.535) total time=   0.1s
[CV 5/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.937, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.938, test=0.536) total time=   0.1s
[CV 7/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.942, test=0.523) total time=   0.2s
[CV 8/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.933, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 10/10] END ccp_alpha=9.259259259259259e-05;, score=(train=0.934, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.935, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.939, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.558) total time=   0.2s
[CV 9/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.559) total time=   0.1s
[CV 10/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.938, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.936, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.935, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.939, test=0.525) total time=   0.1s
[CV 8/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.558) total time=   0.1s
[CV 9/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.933, test=0.559) total time=   0.2s
[CV 10/10] END ccp_alpha=9.270833333333334e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.936, test=0.553) total time=   0.2s
[CV 4/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.934, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.933, test=0.559) total time=   0.1s
[CV 10/10] END ccp_alpha=9.277777777777778e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.934, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.938, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.285714285714287e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.537) total time=   0.2s
[CV 7/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.934, test=0.537) total time=   0.2s
[CV 7/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.285714285714289e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.932, test=0.557) total time=   0.1s
[CV 3/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.936, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.933, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.930, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.932, test=0.560) total time=   0.1s
[CV 10/10] END ccp_alpha=9.293478260869562e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.936, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.938, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.929, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.299233582321816e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.936, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.933, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.929, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.302325581395348e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.936, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.934, test=0.538) total time=   0.2s
[CV 5/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.932, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.933, test=0.538) total time=   0.2s
[CV 7/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.929, test=0.560) total time=   0.2s
[CV 9/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.305555555555556e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.929, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.935, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.934, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.932, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.938, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.929, test=0.560) total time=   0.2s
[CV 9/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.307692307692315e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.2s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.2s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.935, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.33333333333333e-05;, score=(train=0.930, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.937, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.931, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.935, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.933, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.937, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.931, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.339549339549339e-05;, score=(train=0.930, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.931, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 5/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.937, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.929, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.929, test=0.561) total time=   0.1s
[CV 10/10] END ccp_alpha=9.369731081926209e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.375e-05;, score=(train=0.930, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.375e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.375e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.538) total time=   0.2s
[CV 7/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.375e-05;, score=(train=0.930, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.375e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.375e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.375e-05;, score=(train=0.930, test=0.555) total time=   0.1s
[CV 3/10] END ccp_alpha=9.375e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.375e-05;, score=(train=0.931, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=9.375e-05;, score=(train=0.932, test=0.538) total time=   0.1s
[CV 7/10] END ccp_alpha=9.375e-05;, score=(train=0.937, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.375e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.937, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.930, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.934, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.931, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.932, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.937, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.928, test=0.562) total time=   0.1s
[CV 10/10] END ccp_alpha=9.383116883116883e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.936, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.930, test=0.556) total time=   0.1s
[CV 3/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.934, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.932, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.930, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.932, test=0.537) total time=   0.1s
[CV 7/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.937, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.928, test=0.559) total time=   0.2s
[CV 9/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.927, test=0.563) total time=   0.1s
[CV 10/10] END ccp_alpha=9.391369047619043e-05;, score=(train=0.929, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.935, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.933, test=0.552) total time=   0.2s
[CV 4/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.930, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.930, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.935, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.926, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.41666666666667e-05;, score=(train=0.928, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.935, test=0.556) total time=   0.2s
[CV 2/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.932, test=0.553) total time=   0.1s
[CV 4/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.930, test=0.541) total time=   0.2s
[CV 7/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.935, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.925, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.425872093023252e-05;, score=(train=0.928, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.935, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.931, test=0.552) total time=   0.1s
[CV 4/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.930, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.935, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 9/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.925, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.428571428571437e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.935, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.930, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.935, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.925, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.441140792360307e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.934, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.930, test=0.540) total time=   0.1s
[CV 7/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.444444444444447e-05;, score=(train=0.927, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.934, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.928, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.930, test=0.540) total time=   0.1s
[CV 7/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.924, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.449472096530924e-05;, score=(train=0.927, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.934, test=0.555) total time=   0.1s
[CV 2/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.931, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.930, test=0.540) total time=   0.1s
[CV 7/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.452380952380953e-05;, score=(train=0.927, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.934, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.930, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.929, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.929, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.934, test=0.526) total time=   0.1s
[CV 8/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.927, test=0.560) total time=   0.1s
[CV 9/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.924, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.468390804597701e-05;, score=(train=0.927, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.934, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.930, test=0.554) total time=   0.1s
[CV 4/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.931, test=0.540) total time=   0.1s
[CV 5/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.929, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.934, test=0.526) total time=   0.2s
[CV 8/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.927, test=0.560) total time=   0.2s
[CV 9/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.469696969696971e-05;, score=(train=0.927, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.933, test=0.556) total time=   0.1s
[CV 2/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.929, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.931, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.928, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.929, test=0.541) total time=   0.1s
[CV 7/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.934, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.926, test=0.561) total time=   0.1s
[CV 9/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.481481481481488e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.933, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.927, test=0.558) total time=   0.1s
[CV 3/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.929, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.931, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.928, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.929, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.934, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.926, test=0.561) total time=   0.1s
[CV 9/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.923, test=0.564) total time=   0.1s
[CV 10/10] END ccp_alpha=9.496953663620328e-05;, score=(train=0.926, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.5e-05;, score=(train=0.933, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.5e-05;, score=(train=0.927, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.5e-05;, score=(train=0.929, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.5e-05;, score=(train=0.931, test=0.539) total time=   0.2s
[CV 5/10] END ccp_alpha=9.5e-05;, score=(train=0.928, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=9.5e-05;, score=(train=0.929, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.5e-05;, score=(train=0.934, test=0.527) total time=   0.1s
[CV 8/10] END ccp_alpha=9.5e-05;, score=(train=0.926, test=0.561) total time=   0.2s
[CV 9/10] END ccp_alpha=9.5e-05;, score=(train=0.923, test=0.564) total time=   0.2s
[CV 10/10] END ccp_alpha=9.5e-05;, score=(train=0.926, test=0.543) total time=   0.3s
[CV 1/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.933, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.927, test=0.558) total time=   0.2s
[CV 3/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.928, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.930, test=0.539) total time=   0.1s
[CV 5/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.928, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.928, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.933, test=0.527) total time=   0.2s
[CV 8/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.926, test=0.562) total time=   0.1s
[CV 9/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.923, test=0.565) total time=   0.3s
[CV 10/10] END ccp_alpha=9.52380952380952e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.929, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.924, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.926, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.925, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.925, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.930, test=0.530) total time=   0.2s
[CV 8/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.924, test=0.563) total time=   0.2s
[CV 9/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.919, test=0.565) total time=   0.2s
[CV 10/10] END ccp_alpha=9.526695526695527e-05;, score=(train=0.922, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.929, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.923, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.926, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.928, test=0.532) total time=   0.2s
[CV 8/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.924, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.919, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.545454545454543e-05;, score=(train=0.922, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.929, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.923, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.926, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.927, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.928, test=0.532) total time=   0.1s
[CV 8/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.924, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.919, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.54634153908201e-05;, score=(train=0.922, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.928, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.925, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.924, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.927, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.918, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.570664414414413e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.928, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.925, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.927, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.923, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.918, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.573158930621621e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.927, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.917, test=0.567) total time=   0.1s
[CV 10/10] END ccp_alpha=9.583333333333335e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.923, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.926, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.927, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.917, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.587301587301587e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.922, test=0.561) total time=   0.1s
[CV 3/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.925, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.917, test=0.567) total time=   0.1s
[CV 10/10] END ccp_alpha=9.594405594405607e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.922, test=0.561) total time=   0.2s
[CV 3/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.925, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.924, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.924, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.917, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.595160707836776e-05;, score=(train=0.921, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.927, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.922, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.925, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 7/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.923, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.917, test=0.567) total time=   0.2s
[CV 10/10] END ccp_alpha=9.603174603174604e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.922, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.925, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.543) total time=   0.2s
[CV 7/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.917, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.921, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.922, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.925, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 7/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.926, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.923, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.917, test=0.566) total time=   0.1s
[CV 10/10] END ccp_alpha=9.60317460317461e-05;, score=(train=0.921, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.927, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.922, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.924, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.926, test=0.534) total time=   0.2s
[CV 8/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.922, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.917, test=0.567) total time=   0.1s
[CV 10/10] END ccp_alpha=9.619246327443046e-05;, score=(train=0.921, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.925, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.924, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.916, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.633152173913045e-05;, score=(train=0.920, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.922, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.916, test=0.569) total time=   0.2s
[CV 10/10] END ccp_alpha=9.642857142857143e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.925, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.916, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.642857142857153e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.925, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.921, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.923, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.923, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.922, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.924, test=0.533) total time=   0.1s
[CV 8/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.921, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.915, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.648786717752235e-05;, score=(train=0.919, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.925, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.541) total time=   0.2s
[CV 5/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.922, test=0.541) total time=   0.2s
[CV 7/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.923, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.915, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.662337662337661e-05;, score=(train=0.918, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.924, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.923, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.921, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.923, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.915, test=0.568) total time=   0.1s
[CV 10/10] END ccp_alpha=9.688644688644692e-05;, score=(train=0.918, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.2s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.2s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.923, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 7/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.6969696969697e-05;, score=(train=0.918, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.924, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.920, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.920, test=0.543) total time=   0.1s
[CV 7/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.922, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.920, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.914, test=0.569) total time=   0.1s
[CV 10/10] END ccp_alpha=9.705882352941181e-05;, score=(train=0.917, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.921, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.917, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.919, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.919, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.917, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.920, test=0.534) total time=   0.1s
[CV 8/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.918, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.912, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=9.724206349206348e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.921, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.919, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.919, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.917, test=0.545) total time=   0.1s
[CV 7/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.918, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.912, test=0.572) total time=   0.2s
[CV 10/10] END ccp_alpha=9.738095238095232e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.920, test=0.557) total time=   0.2s
[CV 2/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.919, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.920, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.919, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.916, test=0.546) total time=   0.2s
[CV 7/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.918, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.912, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=9.749999999999993e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.920, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.916, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.918, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.920, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.919, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.915, test=0.546) total time=   0.2s
[CV 7/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.918, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.912, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.754689754689749e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.920, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.918, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.919, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.919, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.917, test=0.564) total time=   0.1s
[CV 9/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.912, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.767316017316016e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.920, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.916, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.918, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.919, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.918, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.917, test=0.564) total time=   0.2s
[CV 9/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.912, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=9.76874003189792e-05;, score=(train=0.915, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.920, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.915, test=0.562) total time=   0.2s
[CV 3/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.919, test=0.541) total time=   0.1s
[CV 5/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.918, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.917, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.911, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.781297134238317e-05;, score=(train=0.913, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.919, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.915, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.919, test=0.541) total time=   0.1s
[CV 5/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.917, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.911, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.793382945556856e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.919, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.915, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.917, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.919, test=0.536) total time=   0.1s
[CV 8/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.911, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=9.79757085020243e-05;, score=(train=0.912, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.919, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.915, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.917, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.917, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.915, test=0.546) total time=   0.1s
[CV 7/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.919, test=0.536) total time=   0.2s
[CV 8/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.911, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=9.798367144977767e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.917, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.914, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.918, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.911, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.80392156862745e-05;, score=(train=0.912, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.914, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.918, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.911, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=9.810185185185186e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.919, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.917, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.815934065934065e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.913, test=0.547) total time=   0.2s
[CV 7/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.818181818181817e-05;, score=(train=0.912, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.917, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.911, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=9.821085965786747e-05;, score=(train=0.911, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.917, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.917, test=0.537) total time=   0.2s
[CV 8/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.821858449687057e-05;, score=(train=0.911, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.919, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.915, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.916, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.916, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.911, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.832775919732441e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.917, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.914, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.917, test=0.537) total time=   0.2s
[CV 8/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.916, test=0.565) total time=   0.2s
[CV 9/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.841269841269842e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.914, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.915, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.918, test=0.542) total time=   0.2s
[CV 5/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.916, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.852941176470591e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.915, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.913, test=0.547) total time=   0.2s
[CV 7/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.916, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.855072463768116e-05;, score=(train=0.910, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.915, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.916, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.85614035087718e-05;, score=(train=0.910, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.914, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.915, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.918, test=0.542) total time=   0.1s
[CV 5/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.913, test=0.547) total time=   0.1s
[CV 7/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.916, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.916, test=0.565) total time=   0.1s
[CV 9/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.910, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=9.85749061065517e-05;, score=(train=0.910, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.917, test=0.557) total time=   0.1s
[CV 2/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.914, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.915, test=0.555) total time=   0.2s
[CV 4/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.918, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.915, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.913, test=0.548) total time=   0.1s
[CV 7/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.916, test=0.538) total time=   0.2s
[CV 8/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.915, test=0.566) total time=   0.1s
[CV 9/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.910, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=9.872674183828785e-05;, score=(train=0.908, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.915, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.913, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.915, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.910, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.914, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.914, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.880341880341882e-05;, score=(train=0.907, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.2s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.2s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.909, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.908, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=9.898989898989903e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.911, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.909, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.913, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.913496376811596e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.914, test=0.558) total time=   0.1s
[CV 2/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.912, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.909, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.913, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.919938922469852e-05;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.912, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.911, test=0.555) total time=   0.2s
[CV 6/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.909, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.538) total time=   0.2s
[CV 8/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.913, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.920546932742062e-05;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.914, test=0.558) total time=   0.2s
[CV 2/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.912, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.911, test=0.555) total time=   0.1s
[CV 6/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.909, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.538) total time=   0.2s
[CV 8/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.913, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.908, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.920634920634918e-05;, score=(train=0.906, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.913, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.910, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.909, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.907, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.912, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.907, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=9.958620689655174e-05;, score=(train=0.905, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.910, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.909, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.906, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.537) total time=   0.1s
[CV 8/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.912, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.905, test=0.578) total time=   0.2s
[CV 10/10] END ccp_alpha=9.963768115942034e-05;, score=(train=0.905, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.912, test=0.561) total time=   0.2s
[CV 2/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.910, test=0.562) total time=   0.1s
[CV 3/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.912, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.912, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.909, test=0.556) total time=   0.2s
[CV 6/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.970864661654135e-05;, score=(train=0.905, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.912, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.912, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.911, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.971509971509979e-05;, score=(train=0.905, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.912, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.982638888888888e-05;, score=(train=0.904, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.911, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.912, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 6/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.982916305380078e-05;, score=(train=0.904, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.912, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.907, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.911, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.911, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=9.986772486772487e-05;, score=(train=0.903, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.911, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.907, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.910, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.910, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.905, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=9.999999999999994e-05;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.907, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.906, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.570) total time=   0.1s
[CV 9/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.905, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.912, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.909, test=0.563) total time=   0.2s
[CV 3/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.911, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.907, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.906, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.538) total time=   0.1s
[CV 8/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.910, test=0.570) total time=   0.2s
[CV 9/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.905, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=9.999999999999996e-05;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.907, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001;, score=(train=0.909, test=0.563) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001;, score=(train=0.911, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001;, score=(train=0.907, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.539) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001;, score=(train=0.910, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001;, score=(train=0.905, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001;, score=(train=0.903, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.910, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.907, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.910, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.910, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.906, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.908, test=0.540) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.909, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.904, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010000000000000005;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.909, test=0.559) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.907, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.910, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.910, test=0.543) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.906, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.908, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.909, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.904, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010006473102061337;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.909, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.907, test=0.564) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.910, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.910, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.906, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.908, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.909, test=0.571) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.904, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010008012820512823;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.909, test=0.559) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.906, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.910, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.910, test=0.543) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.904, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.908, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.909, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.904, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010014204545454535;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.908, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.905, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.905, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.907, test=0.569) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.903, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010059523809523811;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.905, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.908, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.907, test=0.569) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.903, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010064484908356272;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.907, test=0.560) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.906, test=0.567) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.902, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010080645161290327;, score=(train=0.899, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.907, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.904, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.903, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.907, test=0.540) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.906, test=0.567) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.902, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001008148148148149;, score=(train=0.899, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.909, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.904, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.903, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.905, test=0.568) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.901, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010083333333333338;, score=(train=0.899, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.907, test=0.560) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.907, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.909, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.904, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.903, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.907, test=0.540) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.905, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.901, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010085356542983656;, score=(train=0.899, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.906, test=0.561) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.905, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.908, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.903, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.902, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.907, test=0.540) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.905, test=0.568) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.901, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010097256090677146;, score=(train=0.898, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.905, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.904, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.907, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.902, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.901, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.906, test=0.541) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.904, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010112076929938584;, score=(train=0.897, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.905, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.904, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.907, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.907, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.900, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010125000000000005;, score=(train=0.897, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000101328320802005;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000101328320802005;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000101328320802005;, score=(train=0.906, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000101328320802005;, score=(train=0.906, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000101328320802005;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000101328320802005;, score=(train=0.900, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000101328320802005;, score=(train=0.905, test=0.543) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000101328320802005;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000101328320802005;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000101328320802005;, score=(train=0.896, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.906, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.906, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.900, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001013689013689013;, score=(train=0.896, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.906, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.906, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.902, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.900, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.905, test=0.543) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.900, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010142843447416474;, score=(train=0.896, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.905, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.906, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.906, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.900, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.900, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010146825396825399;, score=(train=0.896, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.905, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010163398692810458;, score=(train=0.896, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.903, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001016452991452992;, score=(train=0.896, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.903, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010168845315904138;, score=(train=0.896, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.905, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.899, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.899, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010172743230791037;, score=(train=0.895, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.905, test=0.543) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.899, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010173697270471468;, score=(train=0.895, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.906, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.905, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.902, test=0.572) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.899, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010173913043478233;, score=(train=0.895, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.905, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.902, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.905, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.906, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.902, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.899, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.904, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.902, test=0.571) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.899, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010183316080055209;, score=(train=0.895, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.904, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.901, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.903, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.904, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.902, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.897, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010191576689698545;, score=(train=0.894, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.903, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.901, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.903, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.904, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.901, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.897, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010198412698412695;, score=(train=0.894, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.903, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.901, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.903, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.904, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.901, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.897, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010208333333333331;, score=(train=0.894, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.903, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.901, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.903, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.904, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.903, test=0.543) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.901, test=0.572) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.897, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010208333333333334;, score=(train=0.894, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.901, test=0.562) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.900, test=0.565) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.903, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.904, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.900, test=0.557) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.898, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.903, test=0.542) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.901, test=0.572) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.897, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010227272727272727;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.899, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.898, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.902, test=0.544) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.900, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.896, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.899, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.903, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.902, test=0.544) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.900, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.896, test=0.580) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010256410256410253;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.899, test=0.565) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.903, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.898, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.900, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.896, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010260795085356487;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.901, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.899, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.900, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.896, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010273569023569023;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.895, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010285714285714284;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.558) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.897, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.895, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.901, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.898, test=0.558) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.897, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.902, test=0.545) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.900, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.895, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010285714285714287;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.900, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.898, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.901, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.902, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.897, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.896, test=0.550) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.894, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010294017094017086;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.897, test=0.565) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.900, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.902, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.897, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.896, test=0.550) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.901, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.900, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.894, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010303030303030303;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.897, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.897, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.900, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.894, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010315789473684222;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.897, test=0.565) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.902, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.897, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.900, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.900, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.894, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010315873015873015;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.897, test=0.566) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.902, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.897, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.900, test=0.545) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.900, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.894, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001031703019018487;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.896, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.899, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.893, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010332493956486711;, score=(train=0.890, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.898, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.896, test=0.567) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.896, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.899, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.899, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.893, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010337301587301586;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.901, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.899, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.893, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010344610344610343;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.898, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.899, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.895, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.899, test=0.547) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.899, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.893, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001034556396816164;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.898, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.895, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.895, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.899, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.893, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010352061615580548;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.898, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.895, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.893, test=0.579) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010363636363636358;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.898, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.896, test=0.567) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.899, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.901, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.895, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.895, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.898, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.893, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010364372469635463;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.897, test=0.563) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.895, test=0.569) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.897, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.900, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.894, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.894, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.898, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.892, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010370370370370369;, score=(train=0.888, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.897, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.895, test=0.569) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.897, test=0.555) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.899, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.894, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.894, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.895, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.898, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.892, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001037587050630531;, score=(train=0.888, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.897, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.894, test=0.569) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.896, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.899, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.893, test=0.559) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.894, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.894, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.891, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010396619068963307;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.897, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.894, test=0.569) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.896, test=0.557) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.899, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.892, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.894, test=0.553) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.894, test=0.545) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.890, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010406862745098038;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.897, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.893, test=0.570) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.896, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.899, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.892, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.894, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.894, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.890, test=0.578) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001041262484444303;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.897, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.893, test=0.570) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.896, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.899, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.891, test=0.559) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.894, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.894, test=0.545) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.897, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.890, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416308572454768;, score=(train=0.887, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.570) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.895, test=0.556) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.891, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.886, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.570) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.895, test=0.556) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.891, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.893, test=0.547) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416666666666667;, score=(train=0.886, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.896, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.893, test=0.570) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.895, test=0.557) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.891, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.893, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010416666666666669;, score=(train=0.886, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.894, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.891, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.893, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.893, test=0.547) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.896, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.889, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010431060606060606;, score=(train=0.886, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.899, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.890, test=0.560) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.892, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.893, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.896, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.889, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001044015444015443;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.894, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.898, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.890, test=0.560) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.892, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.893, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.896, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010454545454545452;, score=(train=0.884, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.898, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.890, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.891, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.892, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.896, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010462962962962965;, score=(train=0.884, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.896, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.898, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.890, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.891, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.892, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.896, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.888, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001046474108769191;, score=(train=0.884, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.896, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.898, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.890, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.892, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.895, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001047619047619048;, score=(train=0.883, test=0.549) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.896, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.894, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.897, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.892, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.895, test=0.575) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001047853535353535;, score=(train=0.883, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.896, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.895, test=0.575) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010490011794191353;, score=(train=0.883, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.889, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.894, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010499999999999999;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000105;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000105;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000105;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000105;, score=(train=0.889, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000105;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000105;, score=(train=0.891, test=0.546) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000105;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000105;, score=(train=0.882, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000105;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000105;, score=(train=0.893, test=0.571) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000105;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000105;, score=(train=0.889, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000105;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000105;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000105;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000105;, score=(train=0.882, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000105;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000105;, score=(train=0.893, test=0.571) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000105;, score=(train=0.897, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000105;, score=(train=0.889, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000105;, score=(train=0.890, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000105;, score=(train=0.891, test=0.546) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000105;, score=(train=0.894, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000105;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000105;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.892, test=0.572) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.893, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.897, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.890, test=0.549) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010518648018648016;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.892, test=0.572) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.893, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.897, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.890, test=0.549) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010519047619047623;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.891, test=0.573) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.893, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.888, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.890, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.887, test=0.575) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001052244127447379;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.891, test=0.573) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.893, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.888, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.890, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.888, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.887, test=0.575) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010523589437171012;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.891, test=0.574) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.893, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.889, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.888, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010536755621447973;, score=(train=0.882, test=0.547) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.891, test=0.574) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.893, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.889, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.885, test=0.572) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010539243365330325;, score=(train=0.882, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.890, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.892, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.889, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.893, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001054622496147919;, score=(train=0.882, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.890, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.892, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.895, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.888, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.889, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010548547129695242;, score=(train=0.882, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.895, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.890, test=0.575) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.892, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.889, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.893, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.885, test=0.572) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010549450549450533;, score=(train=0.881, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.895, test=0.564) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.890, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.892, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.889, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.891, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.884, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010556298276886515;, score=(train=0.881, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.894, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.889, test=0.574) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.892, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.888, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.889, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.891, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.884, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001056862745098038;, score=(train=0.881, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.894, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.889, test=0.574) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.891, test=0.558) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.895, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.888, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.886, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.888, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.890, test=0.576) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.883, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010578282828282825;, score=(train=0.880, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.893, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.889, test=0.574) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.889, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.894, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.887, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.886, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.887, test=0.550) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.890, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.881, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010618026377217553;, score=(train=0.879, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.888, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.889, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.887, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.886, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.890, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.881, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010643132220795896;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.892, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.888, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.889, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.887, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.886, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.890, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.881, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010643939393939394;, score=(train=0.879, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.886, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.884, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.890, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010654892908126117;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.893, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.886, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.890, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010656288156288157;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.887, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.886, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.890, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010656436487638531;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.892, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.893, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.886, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010666666666666659;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.887, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.885, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.879, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.887, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010666666666666667;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.886, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.885, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001066666666666667;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.880, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.886, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010666666666666671;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.892, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010672579453067265;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.885, test=0.551) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.889, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010676470588235292;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.892, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.886, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.893, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.885, test=0.551) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.889, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010676470588235297;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.891, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.886, test=0.576) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.892, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.885, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.884, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.885, test=0.552) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.888, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001069674629541738;, score=(train=0.879, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.891, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.886, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.888, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.892, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.884, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.885, test=0.552) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.888, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.880, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010710771210771216;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.890, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.886, test=0.575) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.888, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.891, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.888, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.879, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010721710628999606;, score=(train=0.877, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.886, test=0.575) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.887, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.891, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.885, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.887, test=0.576) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.879, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001072460955006886;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.885, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.887, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.891, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.884, test=0.562) total time=   0.3s
[CV 6/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.887, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.879, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010748663101604276;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.885, test=0.576) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.887, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.891, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.884, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.884, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.886, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.879, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010756302521008399;, score=(train=0.876, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.886, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.890, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.884, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.883, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.884, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.886, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.878, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010768518313789944;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.889, test=0.567) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.886, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.890, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.884, test=0.561) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.883, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.884, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.886, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.878, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010769230769230771;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.881, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.883, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.878, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010783216783216774;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.883, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.883, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.878, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010785714285714287;, score=(train=0.875, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.882, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.881, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.877, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010792811203943044;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.889, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.882, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.880, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.877, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010796701424384898;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.880, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.885, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.889, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.882, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.880, test=0.553) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.886, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.877, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010801662106009946;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.880, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.884, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.889, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.883, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.882, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.880, test=0.553) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.877, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010803545261959188;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.888, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.880, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.884, test=0.558) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.889, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.883, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.882, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.880, test=0.553) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.877, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010803997726411516;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.887, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.883, test=0.559) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.888, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.883, test=0.562) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.882, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.880, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.876, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001083206875620794;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.887, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.880, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.883, test=0.559) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.888, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.883, test=0.561) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.882, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.880, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.876, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010833333333333336;, score=(train=0.874, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.886, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.880, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.888, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.883, test=0.562) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.880, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010841836734693877;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.886, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010856990622335898;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.881, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.876, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.883, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010858974358974368;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.883, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010859213250517601;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.879, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.886, test=0.548) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.878, test=0.554) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.885, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.876, test=0.573) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00010863121185701835;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.886, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.879, test=0.579) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.885, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.882, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.881, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.878, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.876, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010864329268292683;, score=(train=0.873, test=0.544) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.886, test=0.565) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.878, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.882, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.884, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.881, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.878, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010878378378378377;, score=(train=0.873, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.886, test=0.564) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.884, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.881, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.878, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010888888888888884;, score=(train=0.873, test=0.544) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.884, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.877, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.885, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010903059931878316;, score=(train=0.873, test=0.543) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.877, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.884, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.881, test=0.563) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.881, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.877, test=0.554) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.884, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010909090909090908;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.885, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.877, test=0.579) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.882, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.884, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.881, test=0.563) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.881, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.877, test=0.554) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.884, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.875, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001090909090909091;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.884, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.875, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.879, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.884, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.880, test=0.565) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.880, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.883, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.874, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010939393939393932;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.884, test=0.563) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.875, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.879, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.884, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.880, test=0.565) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.880, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.883, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.874, test=0.573) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010939393939393943;, score=(train=0.873, test=0.543) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.884, test=0.562) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.875, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.879, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.883, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.878, test=0.565) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.880, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.883, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.873, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001095404296038881;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.883, test=0.565) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.875, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.879, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.883, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.878, test=0.566) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.880, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.876, test=0.555) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.883, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.873, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00010964797913950563;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.882, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.875, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.882, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.877, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.878, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.874, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.872, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011004161573831576;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.882, test=0.566) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.878, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.882, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.877, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.874, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.883, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.872, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011004808678837025;, score=(train=0.871, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.882, test=0.566) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.875, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.882, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.877, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.874, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.883, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.872, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011005481893878338;, score=(train=0.871, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.882, test=0.566) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.882, test=0.544) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.877, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.874, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.883, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.872, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011007092198581562;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.882, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.878, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.882, test=0.544) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.877, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.878, test=0.551) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.874, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.882, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.872, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011021187484602118;, score=(train=0.871, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.882, test=0.566) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.875, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.878, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.881, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.877, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.878, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.874, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.882, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.872, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011026429220709457;, score=(train=0.870, test=0.546) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.873, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.877, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.880, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.875, test=0.567) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.877, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.871, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011085972850678727;, score=(train=0.870, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.873, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.877, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.880, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.875, test=0.567) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.877, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.872, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.880, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.871, test=0.574) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011087586677181746;, score=(train=0.870, test=0.546) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.872, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.877, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.880, test=0.545) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.875, test=0.567) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.877, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.872, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.879, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.871, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011103395061728392;, score=(train=0.869, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.872, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.877, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.870, test=0.574) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001111111111111111;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.551) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.872, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.879, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111112;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.868, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.874, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.876, test=0.551) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.878, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111111111111114;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.880, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.876, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.878, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011111781483106632;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.880, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.871, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.875, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.878, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011116252168883757;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.879, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.878, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011117724867724856;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.879, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.871, test=0.580) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.874, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.878, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011121167602451023;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.879, test=0.568) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.871, test=0.580) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.875, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.878, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.870, test=0.575) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001112637362637363;, score=(train=0.868, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.879, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.871, test=0.581) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.875, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.874, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.876, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.870, test=0.576) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011134749813323207;, score=(train=0.867, test=0.545) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.879, test=0.568) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.871, test=0.581) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.875, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.874, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.876, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.870, test=0.576) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011136363636363633;, score=(train=0.867, test=0.545) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.879, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.870, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.875, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.873, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.876, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011162698412698412;, score=(train=0.867, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.879, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.870, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.875, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.878, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.873, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.876, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011168650793650784;, score=(train=0.866, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.878, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.870, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.875, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.877, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.873, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.872, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.876, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.868, test=0.578) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011183182670987548;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.878, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.870, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.875, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.872, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.872, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.871, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.875, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011194083694083698;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.878, test=0.569) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.870, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.875, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.877, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.872, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.872, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.870, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.874, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.868, test=0.578) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011199709573127303;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.876, test=0.569) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.869, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.874, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.876, test=0.545) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.872, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.872, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.869, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.868, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011225490196078431;, score=(train=0.866, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.876, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.869, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.874, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.876, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.872, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.872, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.869, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.868, test=0.579) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011226380712082984;, score=(train=0.866, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.868, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.872, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.871, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.869, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.868, test=0.580) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011241269841269841;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001125;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001125;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001125;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001125;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001125;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001125;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001125;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001125;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001125;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001125;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001125;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001125;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001125;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001125;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.875, test=0.547) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.871, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011250000000000001;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.873, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.874, test=0.546) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.871, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011252104377104373;, score=(train=0.865, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.868, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.874, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.871, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011254162042175376;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.874, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.868, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.873, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011261140819964323;, score=(train=0.865, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.874, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.867, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.873, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.871, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.871, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.869, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011264863828154956;, score=(train=0.865, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.873, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.872, test=0.561) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.868, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001128199628199628;, score=(train=0.865, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.873, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.872, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.874, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.868, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011287625418060206;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.872, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.865, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.874, test=0.546) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.870, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.867, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.873, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.872, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.874, test=0.546) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.867, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011294117647058812;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.872, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.865, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.872, test=0.548) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.867, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.873, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011303418803418802;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.872, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.863, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.872, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.870, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.867, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011313697008049164;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.872, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.863, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.872, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.871, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.870, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.867, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.872, test=0.579) total time=   0.3s
[CV 9/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011316634479482722;, score=(train=0.864, test=0.547) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.872, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.863, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.870, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.866, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011325231656507005;, score=(train=0.864, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.872, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.863, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.870, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.866, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011328671328671324;, score=(train=0.864, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.863, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.560) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.870, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.866, test=0.557) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.872, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011329101932550209;, score=(train=0.864, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.863, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.871, test=0.568) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.870, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.866, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.872, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011333333333333338;, score=(train=0.864, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.870, test=0.572) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.862, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.870, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.871, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.870, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.869, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.866, test=0.557) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.872, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011360875778299441;, score=(train=0.863, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.870, test=0.572) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.862, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.870, test=0.560) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.870, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.869, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.866, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.872, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001136231884057968;, score=(train=0.863, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.870, test=0.572) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.861, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.870, test=0.561) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.871, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.870, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.869, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.866, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.872, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.865, test=0.581) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011370431893687719;, score=(train=0.862, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.870, test=0.572) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.859, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.869, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.870, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.869, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.869, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.866, test=0.558) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.872, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.863, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011384125509967201;, score=(train=0.862, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.858, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.868, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.870, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.863, test=0.581) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011402923241632925;, score=(train=0.861, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.868, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.870, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.867, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.870, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.863, test=0.582) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011417160462791574;, score=(train=0.859, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.868, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.866, test=0.552) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.865, test=0.559) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.870, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.863, test=0.582) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011428571428571418;, score=(train=0.859, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.866, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.869, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.863, test=0.582) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.859, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.857, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.868, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.870, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.867, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.866, test=0.552) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.869, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.863, test=0.582) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001142857142857143;, score=(train=0.859, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.867, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.867, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.869, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.866, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.866, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.865, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.862, test=0.583) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011458333333333334;, score=(train=0.856, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.867, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.867, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.868, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.866, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.866, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.864, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.862, test=0.583) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011470196470196467;, score=(train=0.856, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.867, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.867, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.868, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.865, test=0.570) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.866, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.864, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.861, test=0.583) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011476650845523411;, score=(train=0.856, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.867, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.856, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.866, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.868, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.864, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.866, test=0.553) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.864, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.860, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001148529411764706;, score=(train=0.856, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.866, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.864, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.863, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.868, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.859, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011529411764705892;, score=(train=0.855, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.855, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.866, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.863, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.862, test=0.570) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.864, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.863, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.868, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.859, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011537940379403797;, score=(train=0.855, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.866, test=0.562) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.863, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.861, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.864, test=0.553) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.863, test=0.559) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.858, test=0.584) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011540045766590397;, score=(train=0.855, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.863, test=0.569) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.866, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.862, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.860, test=0.568) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.863, test=0.554) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.861, test=0.558) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.858, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011548306461692298;, score=(train=0.855, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.855, test=0.586) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.861, test=0.556) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.860, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.858, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011566370336600557;, score=(train=0.854, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.861, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.860, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.868, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.858, test=0.584) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011570389388104393;, score=(train=0.854, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.863, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.855, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.861, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.860, test=0.562) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.868, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.858, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011570512820512823;, score=(train=0.854, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.860, test=0.554) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.859, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.857, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011578947368421057;, score=(train=0.854, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.862, test=0.571) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.859, test=0.560) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.857, test=0.584) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011583181411153626;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.865, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.858, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.859, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011583710407239825;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.862, test=0.571) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.854, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.860, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.858, test=0.560) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011589105339105364;, score=(train=0.853, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.865, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.860, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.858, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.859, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.858, test=0.561) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.866, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001160053981106613;, score=(train=0.853, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.865, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.857, test=0.569) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.859, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.858, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011607053562499121;, score=(train=0.853, test=0.548) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.861, test=0.570) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.854, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.865, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.860, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.857, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.859, test=0.555) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.858, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.866, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.855, test=0.584) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011607142857142857;, score=(train=0.853, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.854, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.864, test=0.562) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.859, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.857, test=0.569) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.858, test=0.555) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.857, test=0.561) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.866, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.855, test=0.585) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011623836996336994;, score=(train=0.853, test=0.548) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.861, test=0.570) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.853, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.864, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.859, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.855, test=0.571) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.857, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.857, test=0.562) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.865, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.854, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001163636363636364;, score=(train=0.853, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.852, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.858, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.854, test=0.572) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.855, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.863, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.850, test=0.585) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011675264550264554;, score=(train=0.851, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.852, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.857, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.854, test=0.572) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.855, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.850, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011682692307692311;, score=(train=0.851, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.857, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.855, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.855, test=0.563) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.850, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001169642857142857;, score=(train=0.851, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.859, test=0.573) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.863, test=0.563) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.857, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.855, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.850, test=0.586) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011696625973276918;, score=(train=0.851, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.857, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.855, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.855, test=0.563) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.850, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011700487012987005;, score=(train=0.851, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.859, test=0.573) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.851, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.863, test=0.563) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.857, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.854, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.855, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.863, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011700854700854701;, score=(train=0.851, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.851, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.861, test=0.565) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.857, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.854, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.849, test=0.586) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011720242744697473;, score=(train=0.851, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.859, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.851, test=0.584) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.860, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.855, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.855, test=0.563) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011724806201550398;, score=(train=0.850, test=0.550) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.859, test=0.573) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.850, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.860, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.855, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.849, test=0.586) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011729094076655053;, score=(train=0.850, test=0.550) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.858, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.850, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.860, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.855, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001173611111111111;, score=(train=0.849, test=0.549) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.858, test=0.573) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.850, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.860, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.854, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.854, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.854, test=0.556) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.855, test=0.563) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.861, test=0.578) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.849, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011739130434782608;, score=(train=0.849, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.857, test=0.575) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.858, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.854, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.852, test=0.573) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.859, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.849, test=0.585) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.857, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.858, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.854, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.852, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.853, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.859, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.849, test=0.585) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011778846153846155;, score=(train=0.848, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.856, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.858, test=0.566) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.853, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.852, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.852, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.853, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.859, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.848, test=0.586) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011804878048780453;, score=(train=0.848, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.856, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.848, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.857, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.853, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.852, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.851, test=0.561) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.852, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.859, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.847, test=0.587) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001183333333333333;, score=(train=0.847, test=0.552) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.854, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.847, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.856, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.852, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.851, test=0.572) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.849, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.851, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.858, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.845, test=0.589) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011855169586262029;, score=(train=0.846, test=0.552) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.852, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.847, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.856, test=0.566) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.852, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.851, test=0.572) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.849, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.850, test=0.566) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.857, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.845, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011867347678040999;, score=(train=0.846, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.851, test=0.576) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.854, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.850, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.850, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.848, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.849, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.855, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.844, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011902187902187913;, score=(train=0.845, test=0.551) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.851, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.854, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.850, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.850, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.848, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.849, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.855, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.844, test=0.589) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001190283400809716;, score=(train=0.845, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.851, test=0.576) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.845, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.853, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.850, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.850, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.848, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.849, test=0.565) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.855, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.844, test=0.589) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011904761904761902;, score=(train=0.845, test=0.551) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.853, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.848, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.849, test=0.565) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.855, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.844, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.552) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.576) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.853, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.850, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.848, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.849, test=0.565) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.855, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.844, test=0.589) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00011904761904761907;, score=(train=0.845, test=0.552) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.849, test=0.574) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.845, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.851, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.848, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.849, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.847, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.845, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.854, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.842, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001195613099771516;, score=(train=0.842, test=0.552) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.846, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.851, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.848, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.849, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.847, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.844, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.854, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.841, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001196700605791515;, score=(train=0.842, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.846, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.851, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.849, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.847, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.844, test=0.564) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.854, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.841, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011974463047522386;, score=(train=0.842, test=0.553) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.846, test=0.575) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.849, test=0.570) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.848, test=0.573) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.847, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.844, test=0.564) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.854, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.841, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011979355716878402;, score=(train=0.842, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.843, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.844, test=0.559) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999996;, score=(train=0.840, test=0.556) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.844, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.556) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.844, test=0.559) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.852, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.556) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.845, test=0.576) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.846, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.848, test=0.573) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.844, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00011999999999999999;, score=(train=0.840, test=0.556) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.844, test=0.577) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.843, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.846, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.847, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.848, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.842, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.840, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012008771929824554;, score=(train=0.840, test=0.555) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.844, test=0.577) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.842, test=0.582) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.845, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.847, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.842, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.843, test=0.565) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.852, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.839, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012019230769230764;, score=(train=0.836, test=0.554) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.842, test=0.578) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.842, test=0.582) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.845, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.847, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.847, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.842, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.843, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.852, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.839, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012025799100369352;, score=(train=0.836, test=0.554) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.842, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.844, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.846, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.842, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.842, test=0.565) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.849, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.837, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012046978531563269;, score=(train=0.836, test=0.554) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.841, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.844, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.845, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.845, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.842, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.841, test=0.566) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.849, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.837, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012064777327935221;, score=(train=0.835, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.841, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.844, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.845, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.842, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.841, test=0.566) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.849, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.837, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012071428571428574;, score=(train=0.835, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.578) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.844, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.844, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.558) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.841, test=0.566) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.837, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012091390091390091;, score=(train=0.833, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.584) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.844, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.843, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.841, test=0.566) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.848, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.837, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012095374931581841;, score=(train=0.833, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.578) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.584) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.844, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.843, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.558) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.841, test=0.566) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.837, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012096774193548393;, score=(train=0.833, test=0.555) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.840, test=0.580) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.843, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.843, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.839, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.836, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001212137883402252;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.840, test=0.580) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.840, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.843, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.845, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.843, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.839, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.836, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001212158448624614;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.840, test=0.580) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.843, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.843, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.839, test=0.557) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.848, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.836, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012131023604812713;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.840, test=0.580) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.840, test=0.583) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.843, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.845, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.843, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.839, test=0.557) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.848, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.831, test=0.590) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012136222910216711;, score=(train=0.832, test=0.553) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.839, test=0.581) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.837, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.843, test=0.567) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.843, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.841, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.836, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.829, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012190476190476193;, score=(train=0.831, test=0.554) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.839, test=0.581) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.837, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.842, test=0.567) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.843, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.840, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.836, test=0.559) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.829, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001221011787357943;, score=(train=0.830, test=0.554) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.841, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.839, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.845, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012239790509287234;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.841, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.839, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.835, test=0.567) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001224080267558528;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.839, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.835, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.845, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012245173745173735;, score=(train=0.829, test=0.557) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.836, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.838, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.835, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.833, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.845, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012250000000000065;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.836, test=0.585) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.837, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.833, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.829, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012272727272727267;, score=(train=0.829, test=0.557) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.836, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.841, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.842, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.837, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.834, test=0.561) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.833, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012272727272727272;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.842, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.837, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.829, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012291612228899906;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.837, test=0.582) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.842, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.837, test=0.576) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.834, test=0.561) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.829, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012291666666666663;, score=(train=0.829, test=0.557) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.837, test=0.582) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.841, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.837, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.828, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012299289711579847;, score=(train=0.828, test=0.558) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.837, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.839, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.840, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.837, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.834, test=0.561) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.833, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.844, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.828, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012304761904761904;, score=(train=0.828, test=0.558) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000123115468409586;, score=(train=0.837, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000123115468409586;, score=(train=0.833, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000123115468409586;, score=(train=0.839, test=0.568) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000123115468409586;, score=(train=0.840, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000123115468409586;, score=(train=0.837, test=0.578) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000123115468409586;, score=(train=0.833, test=0.563) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000123115468409586;, score=(train=0.833, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000123115468409586;, score=(train=0.844, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000123115468409586;, score=(train=0.828, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000123115468409586;, score=(train=0.828, test=0.558) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.837, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.833, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.838, test=0.568) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.840, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.835, test=0.576) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.833, test=0.563) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.843, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.828, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001233135720041462;, score=(train=0.828, test=0.558) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.833, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.831, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.835, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.838, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.831, test=0.565) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.842, test=0.582) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.827, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012363211951447258;, score=(train=0.828, test=0.558) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.830, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.830, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.838, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.831, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.842, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.826, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012385361552028214;, score=(train=0.827, test=0.559) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.830, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.830, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.838, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.831, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.842, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012390823659480377;, score=(train=0.827, test=0.559) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.830, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.829, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.838, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.830, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.832, test=0.569) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001240293637666254;, score=(train=0.825, test=0.561) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.829, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.829, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.835, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.838, test=0.552) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.834, test=0.575) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.841, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.826, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001240894160760083;, score=(train=0.825, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.829, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.828, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.835, test=0.569) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.838, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.832, test=0.569) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.825, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.829, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.828, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.835, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.838, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.834, test=0.575) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.832, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001241025641025641;, score=(train=0.825, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.829, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.827, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.834, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.837, test=0.553) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.830, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.830, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.832, test=0.569) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.841, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012429667519181595;, score=(train=0.825, test=0.560) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.829, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.826, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.834, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.836, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.830, test=0.574) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.829, test=0.562) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.831, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.840, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.826, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012455381886708776;, score=(train=0.825, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.829, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.826, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.834, test=0.569) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.836, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.830, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.829, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.831, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.825, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001245759977554885;, score=(train=0.825, test=0.560) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.826, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.825, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.832, test=0.570) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.836, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.830, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.829, test=0.562) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.827, test=0.566) total time=   0.3s
[CV 8/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.840, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.824, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012479166666666666;, score=(train=0.823, test=0.559) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000125;, score=(train=0.826, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000125;, score=(train=0.824, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000125;, score=(train=0.831, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000125;, score=(train=0.835, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.564) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000125;, score=(train=0.825, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000125;, score=(train=0.839, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000125;, score=(train=0.823, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000125;, score=(train=0.822, test=0.559) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000125;, score=(train=0.826, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000125;, score=(train=0.824, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000125;, score=(train=0.831, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000125;, score=(train=0.835, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.574) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000125;, score=(train=0.828, test=0.564) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000125;, score=(train=0.825, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000125;, score=(train=0.839, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000125;, score=(train=0.823, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000125;, score=(train=0.822, test=0.559) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.825, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.823, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.830, test=0.570) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.832, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.827, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.827, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.824, test=0.567) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.837, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.822, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012534722222222222;, score=(train=0.821, test=0.561) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.822, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.818, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.829, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.832, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.825, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.826, test=0.565) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.823, test=0.567) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.837, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.822, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001255652173913044;, score=(train=0.817, test=0.562) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000125664300064556;, score=(train=0.822, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000125664300064556;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000125664300064556;, score=(train=0.829, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000125664300064556;, score=(train=0.830, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000125664300064556;, score=(train=0.824, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000125664300064556;, score=(train=0.826, test=0.566) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000125664300064556;, score=(train=0.822, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000125664300064556;, score=(train=0.836, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000125664300064556;, score=(train=0.821, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000125664300064556;, score=(train=0.816, test=0.562) total time=   0.3s
[CV 1/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.820, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.828, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.828, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.824, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.823, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.822, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.836, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.820, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012590163934426296;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.820, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.826, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.828, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.824, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.822, test=0.570) total time=   0.3s
[CV 7/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.836, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.820, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012600144787644832;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.819, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.818, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.826, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.828, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.824, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.822, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.835, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.818, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001261231884057971;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.819, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.817, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.826, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.828, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.824, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.822, test=0.571) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.818, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012626262626262632;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.819, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.817, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.826, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.828, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.824, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.822, test=0.571) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.821, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.818, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012629310344827575;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.819, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.814, test=0.586) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.827, test=0.556) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.822, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.817, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001265636179965824;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.819, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.827, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.822, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.817, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012666666666666664;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.827, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.822, test=0.583) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.834, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.817, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001267395264116577;, score=(train=0.816, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.827, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.822, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.822, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.834, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.817, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012678716281032978;, score=(train=0.815, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.825, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.827, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.822, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.815, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.833, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.817, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001268237259816206;, score=(train=0.815, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.817, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.813, test=0.587) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.825, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.825, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.821, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.815, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.833, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.816, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001270588235294118;, score=(train=0.815, test=0.560) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.816, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.813, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.825, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.825, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.821, test=0.570) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.815, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.832, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.816, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012716689001422902;, score=(train=0.812, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.813, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.813, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.825, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.825, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.821, test=0.570) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.814, test=0.568) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.832, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.816, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012742424242424242;, score=(train=0.811, test=0.561) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.813, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.811, test=0.588) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.824, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.824, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.821, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.819, test=0.572) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.814, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.832, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.815, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012770310701956268;, score=(train=0.811, test=0.562) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.813, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.811, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.824, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.824, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.821, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.819, test=0.571) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.814, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.832, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.815, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012773109243697503;, score=(train=0.811, test=0.562) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.813, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.811, test=0.588) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.824, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.823, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.821, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.819, test=0.572) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.814, test=0.568) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.831, test=0.588) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.815, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012785866086539692;, score=(train=0.811, test=0.562) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.809, test=0.587) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.824, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.820, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.820, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.817, test=0.573) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.812, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.831, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.814, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012814238042269203;, score=(train=0.811, test=0.563) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.808, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.824, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.817, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.819, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.817, test=0.573) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.811, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.830, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.813, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012832080200501244;, score=(train=0.810, test=0.565) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.812, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.806, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.815, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.819, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.811, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.829, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.812, test=0.599) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012857142857142852;, score=(train=0.810, test=0.565) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.812, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.815, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.819, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.811, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.829, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.812, test=0.599) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012857142857142858;, score=(train=0.810, test=0.565) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.811, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.815, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.819, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.811, test=0.576) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.825, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.812, test=0.599) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012858225108225083;, score=(train=0.810, test=0.565) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.815, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.819, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.825, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.811, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012864263507782712;, score=(train=0.810, test=0.565) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.811, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.814, test=0.549) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.819, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.810, test=0.575) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.811, test=0.569) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.824, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.811, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001288052943225357;, score=(train=0.809, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.808, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.814, test=0.549) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.818, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.810, test=0.575) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.810, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.824, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.811, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00012890029325513192;, score=(train=0.809, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.808, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.806, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.823, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.814, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.818, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.810, test=0.575) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.810, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.824, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.811, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012893789650612512;, score=(train=0.809, test=0.566) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000129025851737716;, score=(train=0.808, test=0.583) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000129025851737716;, score=(train=0.806, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000129025851737716;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000129025851737716;, score=(train=0.814, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000129025851737716;, score=(train=0.818, test=0.583) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000129025851737716;, score=(train=0.810, test=0.575) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000129025851737716;, score=(train=0.810, test=0.570) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000129025851737716;, score=(train=0.823, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000129025851737716;, score=(train=0.811, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.000129025851737716;, score=(train=0.808, test=0.567) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.804, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.805, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.814, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.817, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.809, test=0.576) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.810, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.820, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.811, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.808, test=0.567) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.804, test=0.583) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.805, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.823, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.814, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.817, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.809, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.810, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.820, test=0.587) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.811, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012929292929292926;, score=(train=0.808, test=0.567) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.805, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.820, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.813, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.815, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.809, test=0.576) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.809, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.820, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.810, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012948281911344482;, score=(train=0.808, test=0.567) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.802, test=0.584) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.805, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.820, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.813, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.815, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.809, test=0.576) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.809, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.820, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.810, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001295171065493646;, score=(train=0.808, test=0.567) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.804, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.820, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.813, test=0.551) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.815, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.807, test=0.579) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.809, test=0.570) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.819, test=0.587) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.809, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001296296296296296;, score=(train=0.807, test=0.566) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000129814921920185;, score=(train=0.801, test=0.586) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000129814921920185;, score=(train=0.804, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000129814921920185;, score=(train=0.819, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000129814921920185;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000129814921920185;, score=(train=0.814, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000129814921920185;, score=(train=0.806, test=0.581) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000129814921920185;, score=(train=0.807, test=0.572) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000129814921920185;, score=(train=0.816, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000129814921920185;, score=(train=0.808, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.000129814921920185;, score=(train=0.807, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.801, test=0.586) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.804, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.819, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.812, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.814, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.806, test=0.581) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.807, test=0.572) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.816, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.807, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00012987477638640426;, score=(train=0.807, test=0.566) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.803, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.817, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.812, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.814, test=0.579) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.805, test=0.580) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.807, test=0.572) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.815, test=0.586) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001302356361316914;, score=(train=0.805, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.803, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.817, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.813, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.804, test=0.580) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.807, test=0.572) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.815, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.806, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013038095238095235;, score=(train=0.805, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.803, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.817, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.813, test=0.582) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.804, test=0.580) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.807, test=0.572) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.814, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001304571147702138;, score=(train=0.804, test=0.569) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.803, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.816, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.802, test=0.583) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.806, test=0.574) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.814, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013066666666666657;, score=(train=0.804, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.803, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.816, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.806, test=0.574) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.814, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.806, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013071895424836603;, score=(train=0.804, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.803, test=0.589) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.816, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.806, test=0.574) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.814, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.806, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013071895424836624;, score=(train=0.804, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.800, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.803, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.816, test=0.578) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.806, test=0.574) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.814, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.805, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001307971014492755;, score=(train=0.804, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.800, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.803, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.816, test=0.578) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.812, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.812, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.802, test=0.584) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.805, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.814, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.805, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013083870967741946;, score=(train=0.804, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.800, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.803, test=0.589) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.815, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.811, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.811, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.802, test=0.584) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.805, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013097456649516982;, score=(train=0.803, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.799, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.802, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.815, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.810, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.810, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.801, test=0.583) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.804, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.813, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013131040934630207;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.799, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.815, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.810, test=0.550) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.810, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.801, test=0.583) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.804, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001313333333333334;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.799, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.815, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.810, test=0.550) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.810, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.804, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013139578503257743;, score=(train=0.803, test=0.568) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.799, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.802, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.814, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.809, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.810, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.801, test=0.585) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.804, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.813, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.802, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001314960010076198;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.799, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.814, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.809, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.810, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.801, test=0.585) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.803, test=0.575) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.813, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.802, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013168756530825487;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.799, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.802, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.813, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.809, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.810, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.801, test=0.586) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.803, test=0.575) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.813, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.801, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001317889317889317;, score=(train=0.803, test=0.568) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.796, test=0.586) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.800, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.811, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.808, test=0.554) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.803, test=0.577) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.802, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.811, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.798, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001324137931034482;, score=(train=0.800, test=0.570) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.811, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.807, test=0.555) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.803, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.802, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.811, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.795, test=0.594) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013251424501424503;, score=(train=0.800, test=0.570) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.795, test=0.587) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.811, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.807, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.803, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.802, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.811, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.795, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013254901960784308;, score=(train=0.800, test=0.570) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.795, test=0.587) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.800, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.811, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.807, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.803, test=0.577) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.798, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.802, test=0.576) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.811, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.795, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001325612642717903;, score=(train=0.800, test=0.570) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.793, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.811, test=0.576) total time=   0.3s
[CV 4/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.805, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.801, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.797, test=0.586) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.802, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.809, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.794, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013303769401330388;, score=(train=0.799, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.792, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.800, test=0.590) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.810, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.805, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.800, test=0.578) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.797, test=0.586) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.802, test=0.576) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.809, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.794, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001332330033003301;, score=(train=0.799, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.800, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.805, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.799, test=0.578) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.797, test=0.586) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.800, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.808, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.792, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001333333333333333;, score=(train=0.798, test=0.569) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.799, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.809, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.805, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.799, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.796, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.800, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.808, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.791, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001333787366968142;, score=(train=0.798, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.791, test=0.585) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.799, test=0.590) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.809, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.804, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.800, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.808, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.791, test=0.593) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013340940389533788;, score=(train=0.798, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.798, test=0.591) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.804, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.799, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.796, test=0.587) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.799, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.808, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.791, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013350936305319729;, score=(train=0.797, test=0.571) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.798, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.804, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.798, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.807, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.790, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001335670908039329;, score=(train=0.797, test=0.572) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.791, test=0.585) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.798, test=0.591) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.809, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.804, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.796, test=0.587) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.798, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.806, test=0.580) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.790, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013358585858585864;, score=(train=0.797, test=0.572) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.789, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.797, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.806, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.803, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.799, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.794, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.798, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.805, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.787, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013384615384615366;, score=(train=0.797, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.789, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.797, test=0.593) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.806, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.803, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.799, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.794, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.797, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.805, test=0.577) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.786, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001339285714285714;, score=(train=0.797, test=0.571) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000134248555281302;, score=(train=0.789, test=0.586) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000134248555281302;, score=(train=0.795, test=0.592) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000134248555281302;, score=(train=0.804, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000134248555281302;, score=(train=0.803, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000134248555281302;, score=(train=0.797, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000134248555281302;, score=(train=0.792, test=0.586) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000134248555281302;, score=(train=0.797, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000134248555281302;, score=(train=0.804, test=0.578) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000134248555281302;, score=(train=0.782, test=0.590) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000134248555281302;, score=(train=0.796, test=0.572) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.787, test=0.590) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.791, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.803, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.796, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.788, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.794, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.798, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.777, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013499999999999997;, score=(train=0.793, test=0.574) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.791, test=0.596) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.803, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.796, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.788, test=0.588) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.794, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.797, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.777, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.793, test=0.574) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.787, test=0.590) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.791, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.803, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.796, test=0.580) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.788, test=0.588) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.794, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.777, test=0.591) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013500000000000003;, score=(train=0.793, test=0.574) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.791, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.803, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.796, test=0.580) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.788, test=0.588) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.794, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.777, test=0.591) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013504273504273502;, score=(train=0.793, test=0.574) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.787, test=0.590) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.788, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.802, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.800, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.793, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.788, test=0.589) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.775, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013554738918301716;, score=(train=0.792, test=0.574) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.785, test=0.588) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.788, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.802, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.799, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.793, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.788, test=0.589) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.797, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.775, test=0.593) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013576437587657793;, score=(train=0.791, test=0.575) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.784, test=0.588) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.787, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.802, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.799, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.793, test=0.581) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.788, test=0.589) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013594405594405607;, score=(train=0.791, test=0.577) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.784, test=0.588) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.787, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.802, test=0.574) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.799, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.793, test=0.581) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.792, test=0.577) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.797, test=0.574) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013605980366830292;, score=(train=0.791, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.783, test=0.590) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.787, test=0.596) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.801, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.799, test=0.557) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.792, test=0.582) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.787, test=0.590) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.792, test=0.577) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.797, test=0.574) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013611111111111107;, score=(train=0.791, test=0.577) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.777, test=0.593) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.786, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.799, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.794, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.790, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.785, test=0.592) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.792, test=0.578) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.795, test=0.577) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.770, test=0.592) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013698624327200407;, score=(train=0.787, test=0.577) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.775, test=0.592) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.784, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.795, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.793, test=0.557) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.790, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.785, test=0.592) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.791, test=0.578) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.793, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.770, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013724449012218797;, score=(train=0.787, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.775, test=0.592) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.784, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.794, test=0.571) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.793, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.790, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.785, test=0.592) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.790, test=0.579) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.793, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.770, test=0.592) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013742808921147912;, score=(train=0.787, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.783, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.789, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.793, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.790, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.784, test=0.593) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.785, test=0.579) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.793, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.769, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001378159961401454;, score=(train=0.786, test=0.575) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.774, test=0.592) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.782, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.787, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.793, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.789, test=0.584) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.784, test=0.593) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.784, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.792, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.769, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001380803127495508;, score=(train=0.786, test=0.575) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.781, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.786, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.783, test=0.592) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.783, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.791, test=0.580) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.767, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013848464753637166;, score=(train=0.784, test=0.576) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.781, test=0.598) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.786, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.782, test=0.593) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.783, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.790, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.767, test=0.598) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013874897047986477;, score=(train=0.784, test=0.576) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.781, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.786, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.782, test=0.593) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.783, test=0.582) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.789, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.767, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013885010757829267;, score=(train=0.784, test=0.576) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.774, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.781, test=0.598) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.786, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.791, test=0.559) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.789, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.782, test=0.593) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.783, test=0.582) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.789, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.767, test=0.598) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013885145253069776;, score=(train=0.784, test=0.576) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.773, test=0.594) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.780, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.783, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.788, test=0.584) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.782, test=0.595) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.782, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.788, test=0.579) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.766, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001391326908821342;, score=(train=0.784, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.779, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.783, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.786, test=0.585) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.782, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.782, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.788, test=0.579) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.766, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013935389587167073;, score=(train=0.783, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.770, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.784, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.765, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.580) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.770, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.778, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.572) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.784, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.765, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013986013986013986;, score=(train=0.782, test=0.580) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.782, test=0.572) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.784, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.780, test=0.596) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.782, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.765, test=0.594) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00013995578889195934;, score=(train=0.782, test=0.580) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.768, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.778, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.782, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.781, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014019230769230767;, score=(train=0.781, test=0.580) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.782, test=0.573) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.790, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.782, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.780, test=0.596) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.781, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.764, test=0.595) total time=   0.3s
[CV 10/10] END ccp_alpha=0.00014023297272661565;, score=(train=0.781, test=0.580) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.768, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.781, test=0.580) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.764, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014028267973856208;, score=(train=0.780, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.781, test=0.580) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.785, test=0.581) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014030961996479231;, score=(train=0.780, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.778, test=0.597) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.780, test=0.596) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.781, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014033068783068773;, score=(train=0.780, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.768, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.778, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.782, test=0.573) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.790, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.782, test=0.587) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.780, test=0.596) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.780, test=0.579) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.785, test=0.581) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.764, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014043163012331382;, score=(train=0.780, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.767, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.777, test=0.596) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.780, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.787, test=0.556) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.781, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.780, test=0.595) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.779, test=0.580) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.784, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.763, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014081031665129536;, score=(train=0.780, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.765, test=0.597) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.776, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.777, test=0.574) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.787, test=0.556) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.781, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.780, test=0.594) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.779, test=0.580) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.783, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.763, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014112499464087846;, score=(train=0.779, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.765, test=0.597) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.775, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.777, test=0.575) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.785, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.780, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.780, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.777, test=0.581) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.782, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.762, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014134615384615395;, score=(train=0.779, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.764, test=0.598) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.777, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.785, test=0.558) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.780, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.780, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.777, test=0.581) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.782, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.762, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014139342139342164;, score=(train=0.779, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.764, test=0.598) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.776, test=0.575) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.785, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.780, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.780, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.777, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.782, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.762, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014142146671558466;, score=(train=0.779, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.764, test=0.598) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.775, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.784, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.779, test=0.585) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.779, test=0.593) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.776, test=0.582) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.780, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.761, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001417106400400307;, score=(train=0.778, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.763, test=0.599) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.774, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.775, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.780, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.778, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.779, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.776, test=0.583) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.780, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014195472003234247;, score=(train=0.778, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.763, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.774, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.775, test=0.576) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.780, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.778, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.779, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.773, test=0.583) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.779, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001420975866589149;, score=(train=0.778, test=0.579) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.763, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.774, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.780, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.778, test=0.586) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.779, test=0.594) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.773, test=0.585) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.779, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.760, test=0.595) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014225933908045983;, score=(train=0.778, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.763, test=0.599) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.773, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.778, test=0.552) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.778, test=0.586) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.777, test=0.594) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.773, test=0.585) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.779, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014234636650272192;, score=(train=0.778, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.763, test=0.599) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.775, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.594) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.769, test=0.589) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.760, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014281739722916227;, score=(train=0.777, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.762, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.775, test=0.576) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.777, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.775, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.768, test=0.589) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.777, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.759, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014285714285714281;, score=(train=0.776, test=0.578) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000142948717948718;, score=(train=0.762, test=0.599) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000142948717948718;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000142948717948718;, score=(train=0.774, test=0.578) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000142948717948718;, score=(train=0.777, test=0.551) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000142948717948718;, score=(train=0.775, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000142948717948718;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000142948717948718;, score=(train=0.768, test=0.589) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000142948717948718;, score=(train=0.777, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000142948717948718;, score=(train=0.759, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000142948717948718;, score=(train=0.776, test=0.578) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.762, test=0.598) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.772, test=0.579) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.776, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.774, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.773, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.756, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014326181781294366;, score=(train=0.774, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.761, test=0.600) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.773, test=0.594) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.772, test=0.579) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.776, test=0.553) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.774, test=0.587) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.776, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.773, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.756, test=0.596) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014328766911957526;, score=(train=0.774, test=0.577) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.758, test=0.602) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.773, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.772, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.776, test=0.554) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.773, test=0.588) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.774, test=0.597) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.767, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.773, test=0.582) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.755, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001436339522546419;, score=(train=0.774, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.758, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.773, test=0.594) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.771, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.773, test=0.588) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.774, test=0.597) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.770, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.754, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014380202283574082;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.758, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.772, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.771, test=0.577) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.588) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.774, test=0.597) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.765, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.768, test=0.583) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.753, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.758, test=0.602) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.772, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.771, test=0.577) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.588) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.774, test=0.597) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.765, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.768, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.753, test=0.597) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001440476190476191;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.758, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.770, test=0.595) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.771, test=0.578) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.775, test=0.555) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.773, test=0.588) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.773, test=0.598) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.765, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.768, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.751, test=0.597) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014427324088341032;, score=(train=0.773, test=0.579) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.757, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.769, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.769, test=0.579) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.774, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.772, test=0.589) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.763, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.764, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.764, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.751, test=0.596) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001449275362318842;, score=(train=0.770, test=0.582) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.757, test=0.603) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.769, test=0.593) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.768, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.773, test=0.556) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.772, test=0.589) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.764, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.764, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.751, test=0.595) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014514695534682202;, score=(train=0.769, test=0.584) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.757, test=0.603) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.769, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.768, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.772, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.772, test=0.590) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.763, test=0.588) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.763, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.748, test=0.599) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001452769367167778;, score=(train=0.769, test=0.584) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.757, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.768, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.768, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.772, test=0.558) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.772, test=0.590) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.763, test=0.588) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.763, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.748, test=0.599) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014532967032967033;, score=(train=0.769, test=0.584) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.756, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.768, test=0.593) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.767, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.770, test=0.560) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.767, test=0.595) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.762, test=0.604) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.762, test=0.592) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.760, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.747, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014589768235949627;, score=(train=0.767, test=0.588) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.755, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.766, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.767, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.770, test=0.560) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.767, test=0.595) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.761, test=0.605) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.760, test=0.593) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.760, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.747, test=0.602) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014603174603174595;, score=(train=0.767, test=0.588) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.755, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.765, test=0.595) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.766, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.770, test=0.560) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.763, test=0.596) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.760, test=0.604) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.759, test=0.591) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.760, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.743, test=0.605) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014626068376068381;, score=(train=0.767, test=0.588) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.755, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.764, test=0.594) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.766, test=0.582) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.769, test=0.561) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.761, test=0.597) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.760, test=0.606) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.758, test=0.592) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.760, test=0.584) total time=   0.3s
[CV 9/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.742, test=0.606) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001465380176346374;, score=(train=0.766, test=0.587) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.753, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.762, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.759, test=0.582) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.768, test=0.562) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.757, test=0.599) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.759, test=0.607) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.756, test=0.595) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.755, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.740, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001476923076923077;, score=(train=0.761, test=0.589) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.753, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.762, test=0.597) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.759, test=0.582) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.765, test=0.560) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.757, test=0.599) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.758, test=0.607) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.754, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.755, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.740, test=0.603) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00014796111901375095;, score=(train=0.761, test=0.589) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.748, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.759, test=0.600) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.756, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.759, test=0.562) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.751, test=0.606) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.756, test=0.605) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.751, test=0.598) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.750, test=0.584) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.736, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014947075912593102;, score=(train=0.758, test=0.591) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.748, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.759, test=0.599) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.756, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.759, test=0.562) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.751, test=0.606) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.756, test=0.604) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.751, test=0.598) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.750, test=0.584) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.736, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00014960187353629963;, score=(train=0.758, test=0.591) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.747, test=0.601) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.757, test=0.601) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.754, test=0.581) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.755, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.749, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.755, test=0.603) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.747, test=0.594) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.748, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.735, test=0.604) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015015842425144827;, score=(train=0.750, test=0.590) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.747, test=0.601) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.757, test=0.601) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.754, test=0.581) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.755, test=0.566) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.749, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.755, test=0.603) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.747, test=0.594) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.748, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.735, test=0.604) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015016129032258086;, score=(train=0.750, test=0.590) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.747, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.755, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.754, test=0.581) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.755, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.749, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.750, test=0.609) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.747, test=0.595) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.747, test=0.585) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.735, test=0.604) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015036890353884295;, score=(train=0.750, test=0.590) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.746, test=0.602) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.755, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.754, test=0.580) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.755, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.749, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.750, test=0.609) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.747, test=0.595) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.747, test=0.585) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.734, test=0.604) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015041407930101603;, score=(train=0.750, test=0.590) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.746, test=0.602) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.755, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.754, test=0.580) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.755, test=0.565) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.748, test=0.607) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.750, test=0.609) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.747, test=0.595) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.746, test=0.586) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.734, test=0.604) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001504937629937628;, score=(train=0.750, test=0.590) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.745, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.752, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.744, test=0.587) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.754, test=0.566) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.748, test=0.607) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.748, test=0.611) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.746, test=0.595) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.741, test=0.589) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.733, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001510460090183613;, score=(train=0.749, test=0.589) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.744, test=0.603) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.752, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.744, test=0.587) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.753, test=0.567) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.748, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.747, test=0.611) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.746, test=0.595) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.739, test=0.590) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.733, test=0.603) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001514316239316243;, score=(train=0.749, test=0.589) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.737, test=0.604) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.751, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.743, test=0.587) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.749, test=0.569) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.747, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.744, test=0.611) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.744, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.738, test=0.591) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.733, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015231585609635346;, score=(train=0.747, test=0.591) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.736, test=0.605) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.747, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.742, test=0.588) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.747, test=0.570) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.747, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.744, test=0.611) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.744, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.737, test=0.591) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.732, test=0.604) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015307203389830514;, score=(train=0.745, test=0.591) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.735, test=0.606) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.745, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.738, test=0.585) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.744, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.746, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.740, test=0.612) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.742, test=0.598) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.732, test=0.593) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001539820491434147;, score=(train=0.743, test=0.595) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.735, test=0.605) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.742, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.738, test=0.585) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.744, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.744, test=0.609) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.740, test=0.612) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.742, test=0.599) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.731, test=0.594) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.728, test=0.605) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015446265938069215;, score=(train=0.742, test=0.594) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.735, test=0.605) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.742, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.737, test=0.585) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.744, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.744, test=0.609) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.740, test=0.612) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.742, test=0.599) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.731, test=0.594) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.728, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015461538461538455;, score=(train=0.742, test=0.594) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.737, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.743, test=0.576) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.744, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.738, test=0.613) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.740, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.726, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015540832049306622;, score=(train=0.738, test=0.598) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.737, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.743, test=0.576) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.744, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.613) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.740, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.726, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.598) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.737, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.732, test=0.590) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.743, test=0.576) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.744, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.613) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.740, test=0.597) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.726, test=0.605) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015555555555555554;, score=(train=0.738, test=0.598) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.730, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.736, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.743, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.738, test=0.614) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.739, test=0.598) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.722, test=0.600) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015592592592592597;, score=(train=0.738, test=0.598) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.729, test=0.611) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.736, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.732, test=0.590) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.741, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.736, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.736, test=0.603) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.729, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.722, test=0.600) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015644049471937515;, score=(train=0.738, test=0.599) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.729, test=0.611) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.735, test=0.602) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.732, test=0.591) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.736, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.736, test=0.603) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.729, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015660652304176636;, score=(train=0.738, test=0.599) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.735, test=0.602) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.731, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.742, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.741, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.735, test=0.616) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.734, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.729, test=0.595) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.718, test=0.602) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015685567878841958;, score=(train=0.737, test=0.599) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.735, test=0.602) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.731, test=0.593) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.742, test=0.577) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.735, test=0.617) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.734, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.728, test=0.596) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001569334893048129;, score=(train=0.737, test=0.599) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.732, test=0.604) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.731, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.741, test=0.577) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.735, test=0.617) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.732, test=0.603) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015719738073449014;, score=(train=0.735, test=0.600) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.731, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.741, test=0.577) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.741, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.735, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001573381847225614;, score=(train=0.735, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.728, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.730, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.740, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.741, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.725, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001576363636363636;, score=(train=0.734, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.727, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.730, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.740, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.740, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.725, test=0.605) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001577487173320507;, score=(train=0.734, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.726, test=0.613) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.729, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.737, test=0.578) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.738, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.724, test=0.606) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.728, test=0.596) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015818799094661161;, score=(train=0.734, test=0.600) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.726, test=0.613) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.729, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.737, test=0.578) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.738, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.724, test=0.606) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.718, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015825087740726473;, score=(train=0.734, test=0.600) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.725, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.732, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.729, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.737, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.738, test=0.608) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.723, test=0.606) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.717, test=0.602) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015846286383567796;, score=(train=0.734, test=0.601) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.723, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.731, test=0.605) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.728, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.737, test=0.578) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.738, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.734, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.722, test=0.607) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.728, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.716, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015878107928882614;, score=(train=0.733, test=0.601) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.723, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.731, test=0.605) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.728, test=0.592) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.737, test=0.578) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.738, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.733, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.722, test=0.607) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.727, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.716, test=0.603) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015889984435714767;, score=(train=0.733, test=0.601) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.723, test=0.614) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.730, test=0.606) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.728, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.731, test=0.579) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.737, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.729, test=0.618) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.718, test=0.608) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.727, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.715, test=0.601) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00015933767408850944;, score=(train=0.732, test=0.602) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.722, test=0.615) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.726, test=0.606) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.728, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.728, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.734, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.728, test=0.617) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.718, test=0.608) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.727, test=0.596) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.715, test=0.601) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00015989331774147222;, score=(train=0.732, test=0.602) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.720, test=0.615) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.723, test=0.609) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.728, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.728, test=0.583) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.734, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.727, test=0.616) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.718, test=0.608) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.727, test=0.596) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.715, test=0.601) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001602019937776576;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.719, test=0.615) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.723, test=0.609) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.727, test=0.591) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.728, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.732, test=0.612) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.724, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.714, test=0.607) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.726, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.715, test=0.601) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00016063755697964753;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.719, test=0.615) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.723, test=0.609) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.727, test=0.591) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.728, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.732, test=0.612) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.724, test=0.616) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.714, test=0.607) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.726, test=0.595) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.715, test=0.601) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001606846869820211;, score=(train=0.729, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.712, test=0.617) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.718, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.723, test=0.590) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.725, test=0.582) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.717, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.709, test=0.611) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.718, test=0.601) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.708, test=0.607) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001640909090909091;, score=(train=0.727, test=0.605) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.711, test=0.620) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.718, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.722, test=0.590) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.725, test=0.582) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.717, test=0.621) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.709, test=0.611) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.718, test=0.601) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.707, test=0.609) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001643738977072311;, score=(train=0.727, test=0.605) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.711, test=0.620) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.718, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.722, test=0.592) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.725, test=0.582) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.717, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.709, test=0.611) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.717, test=0.603) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.707, test=0.609) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001645714285714286;, score=(train=0.722, test=0.606) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000164898975984408;, score=(train=0.711, test=0.620) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000164898975984408;, score=(train=0.718, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000164898975984408;, score=(train=0.721, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000164898975984408;, score=(train=0.725, test=0.581) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000164898975984408;, score=(train=0.727, test=0.610) total time=   0.2s
[CV 6/10] END ccp_alpha=0.000164898975984408;, score=(train=0.717, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000164898975984408;, score=(train=0.708, test=0.610) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000164898975984408;, score=(train=0.715, test=0.604) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000164898975984408;, score=(train=0.707, test=0.609) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000164898975984408;, score=(train=0.721, test=0.608) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.710, test=0.619) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.718, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.721, test=0.593) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.724, test=0.582) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.727, test=0.610) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.717, test=0.621) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.708, test=0.610) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.715, test=0.604) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.707, test=0.609) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016519329745512097;, score=(train=0.721, test=0.608) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.710, test=0.619) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.716, test=0.603) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.719, test=0.591) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.722, test=0.582) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.727, test=0.610) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.716, test=0.623) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.707, test=0.610) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.715, test=0.604) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.706, test=0.610) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001659740468799814;, score=(train=0.721, test=0.608) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.710, test=0.620) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.715, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.710, test=0.595) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.718, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.725, test=0.608) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.716, test=0.623) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.707, test=0.610) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.714, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.705, test=0.612) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016713569322737015;, score=(train=0.717, test=0.610) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.710, test=0.620) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.714, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.710, test=0.597) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.718, test=0.583) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.724, test=0.609) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.716, test=0.623) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.707, test=0.610) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.713, test=0.601) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.704, test=0.612) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016742825717428215;, score=(train=0.714, test=0.609) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.709, test=0.621) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.714, test=0.603) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.709, test=0.598) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.716, test=0.585) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.724, test=0.609) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.713, test=0.626) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.704, test=0.613) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.713, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.704, test=0.612) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00016817242738039698;, score=(train=0.713, test=0.609) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.706, test=0.622) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.711, test=0.604) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.703, test=0.603) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.713, test=0.588) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.723, test=0.612) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.710, test=0.624) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.705, test=0.608) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.699, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017073768531802988;, score=(train=0.710, test=0.610) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.704, test=0.622) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.710, test=0.604) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.701, test=0.604) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.712, test=0.588) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.719, test=0.616) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.708, test=0.626) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.702, test=0.613) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.699, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017171866594943488;, score=(train=0.700, test=0.615) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.701, test=0.626) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.708, test=0.605) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.700, test=0.607) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.711, test=0.591) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.715, test=0.618) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.708, test=0.627) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.700, test=0.614) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.699, test=0.617) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017285499566104356;, score=(train=0.695, test=0.619) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.701, test=0.626) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.705, test=0.607) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.699, test=0.608) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.711, test=0.591) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.715, test=0.618) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.707, test=0.628) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.701, test=0.614) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.693, test=0.618) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.697, test=0.617) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017352039201744504;, score=(train=0.694, test=0.619) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.695, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.703, test=0.611) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.699, test=0.609) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.709, test=0.591) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.710, test=0.620) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.707, test=0.627) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.700, test=0.612) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.686, test=0.621) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.695, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017549783972881094;, score=(train=0.692, test=0.619) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.691, test=0.629) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.702, test=0.611) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.698, test=0.610) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.708, test=0.590) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.706, test=0.619) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.699, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.696, test=0.612) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.685, test=0.623) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.690, test=0.616) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00017729627470850336;, score=(train=0.689, test=0.622) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.690, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.696, test=0.617) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.690, test=0.606) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.702, test=0.595) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.704, test=0.618) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.698, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.693, test=0.619) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.684, test=0.622) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.685, test=0.621) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00018000000000000004;, score=(train=0.685, test=0.623) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.690, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.696, test=0.615) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.683, test=0.618) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.702, test=0.595) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.697, test=0.619) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.697, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.687, test=0.627) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.682, test=0.624) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.685, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00018192640692640696;, score=(train=0.685, test=0.623) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.690, test=0.628) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.696, test=0.614) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.681, test=0.621) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.701, test=0.595) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.696, test=0.619) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.695, test=0.632) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.686, test=0.628) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.682, test=0.624) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.685, test=0.622) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00018272889729651146;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.690, test=0.628) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.693, test=0.619) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.679, test=0.622) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.700, test=0.595) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.696, test=0.621) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.695, test=0.632) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.684, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.680, test=0.624) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.683, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00018356733881005742;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000184705754624993;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000184705754624993;, score=(train=0.693, test=0.619) total time=   0.2s
[CV 3/10] END ccp_alpha=0.000184705754624993;, score=(train=0.679, test=0.622) total time=   0.1s
[CV 4/10] END ccp_alpha=0.000184705754624993;, score=(train=0.693, test=0.606) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000184705754624993;, score=(train=0.693, test=0.620) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000184705754624993;, score=(train=0.694, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.000184705754624993;, score=(train=0.680, test=0.629) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000184705754624993;, score=(train=0.680, test=0.624) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000184705754624993;, score=(train=0.683, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000184705754624993;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.691, test=0.616) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.678, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.693, test=0.606) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.692, test=0.622) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.694, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.679, test=0.629) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.680, test=0.624) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.683, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00018527141790006208;, score=(train=0.685, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.687, test=0.631) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.689, test=0.618) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.678, test=0.624) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.693, test=0.606) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.691, test=0.621) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.694, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.678, test=0.626) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.678, test=0.625) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.682, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001862927520822256;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.684, test=0.630) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.686, test=0.614) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.678, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.693, test=0.605) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.691, test=0.621) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.692, test=0.631) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.676, test=0.625) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.678, test=0.625) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.682, test=0.620) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0001870286564000824;, score=(train=0.685, test=0.625) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.679, test=0.634) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.683, test=0.618) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.677, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.689, test=0.610) total time=   0.3s
[CV 5/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.683, test=0.625) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.691, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.675, test=0.628) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.675, test=0.629) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.681, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00019213516199935096;, score=(train=0.683, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.634) total time=   0.3s
[CV 2/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.682, test=0.618) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.627) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.686, test=0.610) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.682, test=0.627) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.690, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.628) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.675, test=0.629) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.681, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00019454594873944894;, score=(train=0.683, test=0.627) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.675, test=0.634) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.680, test=0.623) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.675, test=0.627) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.686, test=0.610) total time=   0.3s
[CV 5/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.681, test=0.629) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.690, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.674, test=0.629) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.675, test=0.629) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.681, test=0.621) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001949484839347121;, score=(train=0.683, test=0.627) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.674, test=0.635) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.676, test=0.623) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.675, test=0.629) total time=   0.3s
[CV 4/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.685, test=0.611) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.679, test=0.631) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.687, test=0.634) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.672, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.672, test=0.630) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0001988604000107675;, score=(train=0.681, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.674, test=0.635) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.675, test=0.623) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.674, test=0.630) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.685, test=0.611) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.678, test=0.634) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.686, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.671, test=0.630) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.670, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00019967166979362107;, score=(train=0.679, test=0.626) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.673, test=0.638) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.674, test=0.625) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.674, test=0.630) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.685, test=0.611) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.678, test=0.634) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.686, test=0.632) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.671, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020074375747267809;, score=(train=0.678, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.673, test=0.638) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.673, test=0.625) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.670, test=0.632) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.683, test=0.611) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.678, test=0.634) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.685, test=0.632) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.671, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.670, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.679, test=0.623) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020156795414693132;, score=(train=0.677, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.672, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.670, test=0.628) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.669, test=0.632) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.682, test=0.614) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.676, test=0.636) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.678, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.667, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.669, test=0.631) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.675, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00020482800933567524;, score=(train=0.674, test=0.631) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.672, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.666, test=0.632) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.669, test=0.632) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.682, test=0.614) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.676, test=0.636) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.678, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.667, test=0.632) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.669, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.674, test=0.626) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020548181172061648;, score=(train=0.674, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.672, test=0.639) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.665, test=0.633) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.668, test=0.635) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.682, test=0.614) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.676, test=0.636) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.678, test=0.633) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.667, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.669, test=0.632) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.674, test=0.626) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00020708431133528628;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.672, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.664, test=0.632) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.668, test=0.637) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.681, test=0.612) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.675, test=0.637) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.678, test=0.633) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.667, test=0.632) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.669, test=0.632) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.674, test=0.626) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00020829876525329852;, score=(train=0.674, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.664, test=0.634) total time=   0.3s
[CV 3/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.668, test=0.637) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.680, test=0.613) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.675, test=0.637) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.675, test=0.637) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.667, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.667, test=0.631) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.674, test=0.626) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00021151725706467644;, score=(train=0.669, test=0.635) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.670, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.663, test=0.633) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.678, test=0.616) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.673, test=0.639) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.672, test=0.641) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.663, test=0.631) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.665, test=0.628) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.670, test=0.629) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00021839080459770113;, score=(train=0.661, test=0.639) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.670, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.663, test=0.633) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.666, test=0.637) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.678, test=0.616) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.673, test=0.639) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.672, test=0.641) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.663, test=0.631) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.662, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.668, test=0.629) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00021924595990980845;, score=(train=0.661, test=0.639) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.669, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.662, test=0.634) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.666, test=0.637) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.672, test=0.622) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.673, test=0.639) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.668, test=0.642) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.662, test=0.631) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.662, test=0.627) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.667, test=0.633) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00022553189386920192;, score=(train=0.660, test=0.636) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.661, test=0.642) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.661, test=0.640) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.663, test=0.639) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.670, test=0.621) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.667, test=0.646) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.665, test=0.642) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.660, test=0.630) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.661, test=0.626) total time=   0.2s
[CV 9/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.666, test=0.631) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00023140005894984987;, score=(train=0.659, test=0.638) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.642) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.640) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.639) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.670, test=0.621) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.666, test=0.646) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.665, test=0.642) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.660, test=0.630) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.661, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.664, test=0.631) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00023309552976813475;, score=(train=0.659, test=0.638) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.659, test=0.640) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.659, test=0.640) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.661, test=0.627) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.665, test=0.645) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.660, test=0.651) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.656, test=0.629) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.661, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.664, test=0.631) total time=   0.2s
[CV 10/10] END ccp_alpha=0.00024019130238910578;, score=(train=0.658, test=0.637) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.659, test=0.640) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.656, test=0.641) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.661, test=0.627) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.665, test=0.645) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.660, test=0.651) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.656, test=0.629) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.661, test=0.626) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.664, test=0.631) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0002410610594676156;, score=(train=0.658, test=0.637) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.643) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.655, test=0.640) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.658, test=0.630) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.658, test=0.652) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.651) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.654, test=0.634) total time=   0.2s
[CV 8/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.661, test=0.624) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.661, test=0.629) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0002577008335828551;, score=(train=0.656, test=0.639) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000286560417042489;, score=(train=0.647, test=0.636) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.642) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000286560417042489;, score=(train=0.653, test=0.624) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000286560417042489;, score=(train=0.650, test=0.654) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.651) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000286560417042489;, score=(train=0.649, test=0.634) total time=   0.1s
[CV 8/10] END ccp_alpha=0.000286560417042489;, score=(train=0.657, test=0.623) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000286560417042489;, score=(train=0.657, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000286560417042489;, score=(train=0.652, test=0.638) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.649, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.644, test=0.634) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.650, test=0.624) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.652, test=0.651) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.649, test=0.634) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.650, test=0.615) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.654, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0003057648802067964;, score=(train=0.651, test=0.638) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.646, test=0.645) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.641, test=0.638) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.649, test=0.625) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.649, test=0.654) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.648, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.648, test=0.617) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0003328293681448338;, score=(train=0.650, test=0.636) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.641, test=0.638) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 4/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.649, test=0.625) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.648, test=0.632) total time=   0.1s
[CV 8/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.648, test=0.617) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00033308536706041017;, score=(train=0.650, test=0.636) total time=   0.2s
[CV 1/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.646, test=0.645) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.641, test=0.639) total time=   0.2s
[CV 3/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.638, test=0.631) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.648, test=0.624) total time=   0.2s
[CV 5/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.640, test=0.645) total time=   0.2s
[CV 6/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.645, test=0.655) total time=   0.1s
[CV 7/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.644, test=0.633) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.643, test=0.611) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.646, test=0.635) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00039473747570871925;, score=(train=0.643, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.000414271464091287;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.000414271464091287;, score=(train=0.640, test=0.638) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000414271464091287;, score=(train=0.638, test=0.631) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000414271464091287;, score=(train=0.648, test=0.624) total time=   0.2s
[CV 5/10] END ccp_alpha=0.000414271464091287;, score=(train=0.639, test=0.644) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000414271464091287;, score=(train=0.645, test=0.655) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000414271464091287;, score=(train=0.644, test=0.633) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000414271464091287;, score=(train=0.643, test=0.611) total time=   0.2s
[CV 9/10] END ccp_alpha=0.000414271464091287;, score=(train=0.646, test=0.635) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000414271464091287;, score=(train=0.643, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.640, test=0.637) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.638, test=0.631) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.647, test=0.621) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.637, test=0.642) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.645, test=0.655) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.644, test=0.633) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.641, test=0.614) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.645, test=0.635) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0004412862035683418;, score=(train=0.643, test=0.632) total time=   0.1s
[CV 1/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.646, test=0.645) total time=   0.1s
[CV 2/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.640, test=0.637) total time=   0.1s
[CV 3/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.638, test=0.631) total time=   0.2s
[CV 4/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.646, test=0.620) total time=   0.1s
[CV 5/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.636, test=0.641) total time=   0.1s
[CV 6/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.645, test=0.655) total time=   0.2s
[CV 7/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.642, test=0.634) total time=   0.2s
[CV 8/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.641, test=0.614) total time=   0.1s
[CV 9/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.645, test=0.635) total time=   0.1s
[CV 10/10] END ccp_alpha=0.00045143542675384063;, score=(train=0.643, test=0.632) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.638, test=0.644) total time=   0.2s
[CV 2/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.632, test=0.632) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.635, test=0.633) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.638, test=0.615) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.636, test=0.641) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.636, test=0.649) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.640, test=0.636) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.638, test=0.607) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.633, test=0.630) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0006066378280838031;, score=(train=0.637, test=0.631) total time=   0.2s
[CV 1/10] END ccp_alpha=0.000674509767592063;, score=(train=0.636, test=0.643) total time=   0.1s
[CV 2/10] END ccp_alpha=0.000674509767592063;, score=(train=0.632, test=0.632) total time=   0.1s
[CV 3/10] END ccp_alpha=0.000674509767592063;, score=(train=0.629, test=0.628) total time=   0.2s
[CV 4/10] END ccp_alpha=0.000674509767592063;, score=(train=0.632, test=0.609) total time=   0.1s
[CV 5/10] END ccp_alpha=0.000674509767592063;, score=(train=0.636, test=0.641) total time=   0.1s
[CV 6/10] END ccp_alpha=0.000674509767592063;, score=(train=0.629, test=0.643) total time=   0.1s
[CV 7/10] END ccp_alpha=0.000674509767592063;, score=(train=0.637, test=0.634) total time=   0.2s
[CV 8/10] END ccp_alpha=0.000674509767592063;, score=(train=0.633, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.000674509767592063;, score=(train=0.630, test=0.628) total time=   0.1s
[CV 10/10] END ccp_alpha=0.000674509767592063;, score=(train=0.637, test=0.631) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.626, test=0.633) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.630, test=0.633) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.625, test=0.624) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.632, test=0.609) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.625, test=0.639) total time=   0.2s
[CV 6/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.629, test=0.643) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.637, test=0.634) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.633, test=0.602) total time=   0.1s
[CV 9/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.630, test=0.628) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0007759198857809069;, score=(train=0.627, test=0.625) total time=   0.1s
[CV 1/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.633) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.616, test=0.621) total time=   0.1s
[CV 3/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.624, test=0.623) total time=   0.1s
[CV 4/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.628, test=0.607) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.625, test=0.639) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.615, test=0.621) total time=   0.2s
[CV 7/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.633) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.629, test=0.599) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.625) total time=   0.2s
[CV 10/10] END ccp_alpha=0.0012379310269602165;, score=(train=0.626, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.625, test=0.632) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.615, test=0.620) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.624, test=0.623) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.618, test=0.595) total time=   0.1s
[CV 5/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.614, test=0.628) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.615, test=0.621) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.615, test=0.619) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.629, test=0.599) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.626, test=0.625) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0013627749235944342;, score=(train=0.626, test=0.624) total time=   0.2s
[CV 1/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.602, test=0.610) total time=   0.1s
[CV 2/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.602, test=0.606) total time=   0.2s
[CV 3/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.614, test=0.616) total time=   0.2s
[CV 4/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.605, test=0.582) total time=   0.2s
[CV 5/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.614, test=0.628) total time=   0.1s
[CV 6/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.602, test=0.608) total time=   0.1s
[CV 7/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.603, test=0.602) total time=   0.1s
[CV 8/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.618, test=0.591) total time=   0.2s
[CV 9/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.615, test=0.619) total time=   0.1s
[CV 10/10] END ccp_alpha=0.0026122163955970557;, score=(train=0.603, test=0.597) total time=   0.2s
[CV 1/10] END ccp_alpha=0.003213844189509124;, score=(train=0.594, test=0.603) total time=   0.2s
[CV 2/10] END ccp_alpha=0.003213844189509124;, score=(train=0.595, test=0.598) total time=   0.1s
[CV 3/10] END ccp_alpha=0.003213844189509124;, score=(train=0.600, test=0.606) total time=   0.2s
[CV 4/10] END ccp_alpha=0.003213844189509124;, score=(train=0.597, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.003213844189509124;, score=(train=0.601, test=0.617) total time=   0.2s
[CV 6/10] END ccp_alpha=0.003213844189509124;, score=(train=0.595, test=0.595) total time=   0.1s
[CV 7/10] END ccp_alpha=0.003213844189509124;, score=(train=0.603, test=0.602) total time=   0.1s
[CV 8/10] END ccp_alpha=0.003213844189509124;, score=(train=0.604, test=0.588) total time=   0.1s
[CV 9/10] END ccp_alpha=0.003213844189509124;, score=(train=0.602, test=0.609) total time=   0.2s
[CV 10/10] END ccp_alpha=0.003213844189509124;, score=(train=0.596, test=0.588) total time=   0.1s
[CV 1/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 2/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.1s
[CV 3/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.1s
[CV 4/10] END ccp_alpha=0.020034588772265882;, score=(train=0.597, test=0.573) total time=   0.1s
[CV 5/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 6/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.1s
[CV 7/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 8/10] END ccp_alpha=0.020034588772265882;, score=(train=0.596, test=0.583) total time=   0.1s
[CV 9/10] END ccp_alpha=0.020034588772265882;, score=(train=0.500, test=0.500) total time=   0.2s
[CV 10/10] END ccp_alpha=0.020034588772265882;, score=(train=0.596, test=0.588) total time=   0.2s

GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=DecisionTreeClassifier(random_state=42),
             param_grid={'ccp_alpha': array([0.00000000e+00, 2.66666667e-05, 2.85714286e-05, ...,
       2.61221640e-03, 3.21384419e-03, 2.00345888e-02])},
             return_train_score=True, scoring='roc_auc', verbose=4)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Model Performance

report_GridSearchCV_results(grid_search_post_prune)

- Best combination of hyperparams:
 {'ccp_alpha': 0.0002577008335828551} 

- Best mean_test_score:
 0.6385992855021885 

- Score by fold for best estimator:
 [0.6445609038774083, 0.6429567488600647, 0.64027184707424, 0.6303182836899942, 0.6515900666379598, 0.6506916876817452, 0.6337261049457951, 0.6242858044524325, 0.6286588306845862, 0.6389325771176594] 

- Top 10 hyperparameter combinations by mean_test_score:

	mean_test_score	param_ccp_alpha
rank_test_score
1	0.638599	0.000258
2	0.637341	0.000241
3	0.637245	0.000240
4	0.637043	0.000287
5	0.636850	0.000333
5	0.636850	0.000333
7	0.636076	0.000306
8	0.635346	0.000233
9	0.635314	0.000231
10	0.634850	0.000395

compare_performance(grid_search_post_prune)

	train_AUC	val_AUC
1	1.0	0.533032
2	1.0	0.533008
3	1.0	0.533080
4	1.0	0.533080
5	1.0	0.533080
6	1.0	0.533079
7	1.0	0.533079
8	1.0	0.533109
9	1.0	0.533109
10	1.0	0.533109
Mean	1.0	0.533077

best_model_post_prune=grid_search_post_prune.best_estimator_

plot_probability_std(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")

plot_avg_feature_importance(best_model_post_prune, df_train, y_name, x_name, kf10, "Post-pruned Classification Tree")

evaluate_model(best_model_post_prune, df_X_test_scaled, df_y_test)

Test AUC: 0.64
Accuracy: 0.60
Confusion Matrix:
[[1843  815]
 [1179 1163]]

Classification Report:
              precision    recall  f1-score   support

           0       0.61      0.69      0.65      2658
           1       0.59      0.50      0.54      2342

    accuracy                           0.60      5000
   macro avg       0.60      0.59      0.59      5000
weighted avg       0.60      0.60      0.60      5000

plot_roc_curve(best_model_post_prune, df_X_test_scaled, df_y_test)

# Plotting the tree
plt.figure(figsize=(50, 20))
plot_tree(best_model_post_prune, filled=True, feature_names=X_train.columns, class_names=['Not readmitted', 'Readmitted'], rounded=True)
plt.show()

Random Forest Model (with regularization)

# Initialize model
randomforest = RandomForestClassifier(max_depth = 6, random_state = 42, bootstrap=True)

# Define the hyperparameter grid
rf_param_grid = {
    'max_depth': [2, 3, 4],
    'min_samples_leaf': [500, 1000, 2000],
    'max_features': [2, 3], 
}

# Create a GridSearchCV object
grid_search_rf = GridSearchCV(estimator=randomforest, param_grid=rf_param_grid, cv=kf10, scoring='roc_auc', verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
# To resolve error: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of 
# y to (n_samples,), for example using ravel().return fit_method(estimator, *args, **kwargs)
grid_search_rf.fit(df_train[x_name], df_train[y_name].values.ravel())

Fitting 10 folds for each of 18 candidates, totalling 180 fits
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.639) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.647) total time=   0.3s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.639) total time=   0.3s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.615) total time=   0.4s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.643) total time=   0.5s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.639, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.635) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.612) total time=   0.3s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.640, test=0.628) total time=   0.3s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=500;, score=(train=0.641, test=0.639) total time=   0.3s
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.639) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.647) total time=   0.3s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.640) total time=   0.3s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.615) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.643) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.649) total time=   0.3s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.634) total time=   0.3s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.641, test=0.612) total time=   0.3s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.630) total time=   0.3s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.636) total time=   0.3s
[CV 1/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.617) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.3s
[CV 3/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.625) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.627, test=0.600) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.624, test=0.628) total time=   0.3s
[CV 6/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.635) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.614) total time=   0.3s
[CV 8/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.610) total time=   0.5s
[CV 9/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.624) total time=   0.3s
[CV 10/10] END max_depth=2, max_features=2, min_samples_leaf=2000;, score=(train=0.626, test=0.613) total time=   0.3s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.648) total time=   0.3s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.657) total time=   0.5s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.644, test=0.645) total time=   0.5s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.649, test=0.618) total time=   0.4s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.654) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.652) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.643) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.648, test=0.616) total time=   0.4s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.629) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.647) total time=   0.4s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.646) total time=   0.4s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.656) total time=   0.4s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.643) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.615) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.651) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.644, test=0.642) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.645, test=0.614) total time=   0.4s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.626) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.644) total time=   0.4s
[CV 1/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.624, test=0.622) total time=   0.4s
[CV 2/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.639) total time=   0.4s
[CV 3/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.623, test=0.623) total time=   0.4s
[CV 4/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.629, test=0.601) total time=   0.3s
[CV 5/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.630) total time=   0.4s
[CV 6/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.622, test=0.632) total time=   0.4s
[CV 7/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.622) total time=   0.4s
[CV 8/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.611) total time=   0.4s
[CV 9/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.625, test=0.618) total time=   0.4s
[CV 10/10] END max_depth=2, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.615) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.642) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.651) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.644) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.647, test=0.615) total time=   0.3s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.642, test=0.648) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.643, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.638) total time=   0.4s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.616) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.633) total time=   0.4s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.642) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.639) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.647) total time=   0.3s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.641) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.614) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.637, test=0.643) total time=   0.3s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.647) total time=   0.3s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.634) total time=   0.3s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.641, test=0.613) total time=   0.5s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.631) total time=   0.4s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.634) total time=   0.3s
[CV 1/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.615) total time=   0.3s
[CV 2/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.3s
[CV 3/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.623) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.627, test=0.599) total time=   0.3s
[CV 5/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.628) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.633) total time=   0.3s
[CV 7/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.3s
[CV 8/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.624, test=0.609) total time=   0.3s
[CV 9/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.624) total time=   0.4s
[CV 10/10] END max_depth=3, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.5s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.647) total time=   0.6s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.653) total time=   0.5s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.647) total time=   0.6s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.650, test=0.615) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.654) total time=   0.6s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.649) total time=   0.4s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.640) total time=   0.5s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.649, test=0.618) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.630) total time=   0.5s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.651) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.642) total time=   0.3s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.652) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.644) total time=   0.5s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.614) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.647) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.648) total time=   0.4s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.639) total time=   0.4s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.645, test=0.617) total time=   0.4s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.641, test=0.626) total time=   0.3s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.644) total time=   0.4s
[CV 1/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.625) total time=   0.4s
[CV 2/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.640) total time=   0.4s
[CV 3/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.624, test=0.626) total time=   0.4s
[CV 4/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.631, test=0.600) total time=   0.4s
[CV 5/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.631) total time=   0.4s
[CV 6/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.623, test=0.633) total time=   0.3s
[CV 7/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.624) total time=   0.3s
[CV 8/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.614) total time=   0.3s
[CV 9/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.619) total time=   0.3s
[CV 10/10] END max_depth=3, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.617) total time=   0.4s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.648) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.650) total time=   0.4s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.645, test=0.644) total time=   0.3s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.648, test=0.613) total time=   0.4s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.651) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.644, test=0.650) total time=   0.5s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.641) total time=   0.5s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.647, test=0.615) total time=   0.3s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.634) total time=   0.4s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=500;, score=(train=0.646, test=0.644) total time=   0.4s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.639) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.648) total time=   0.3s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.643) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.611) total time=   0.3s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.643) total time=   0.3s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.638, test=0.647) total time=   0.4s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.633) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.642, test=0.611) total time=   0.4s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.640, test=0.631) total time=   0.4s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=1000;, score=(train=0.639, test=0.635) total time=   0.3s
[CV 1/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.622, test=0.615) total time=   0.3s
[CV 2/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.638) total time=   0.3s
[CV 3/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.623) total time=   0.3s
[CV 4/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.627, test=0.599) total time=   0.3s
[CV 5/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.628) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.621, test=0.633) total time=   0.3s
[CV 7/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.3s
[CV 8/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.624, test=0.609) total time=   0.3s
[CV 9/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.623, test=0.624) total time=   0.3s
[CV 10/10] END max_depth=4, max_features=2, min_samples_leaf=2000;, score=(train=0.625, test=0.613) total time=   0.3s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.648) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.651) total time=   0.5s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.648) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.651, test=0.616) total time=   0.4s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.646, test=0.655) total time=   0.5s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.651) total time=   0.4s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.645, test=0.639) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.651, test=0.620) total time=   0.6s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.631) total time=   0.5s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=500;, score=(train=0.647, test=0.650) total time=   0.5s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.643) total time=   0.4s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.653) total time=   0.5s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.647) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.615) total time=   0.5s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.642, test=0.648) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.644, test=0.649) total time=   0.5s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.644, test=0.639) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.646, test=0.620) total time=   0.4s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.628) total time=   0.4s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=1000;, score=(train=0.643, test=0.642) total time=   0.4s
[CV 1/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.625) total time=   0.3s
[CV 2/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.640) total time=   0.4s
[CV 3/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.624, test=0.626) total time=   0.4s
[CV 4/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.631, test=0.600) total time=   0.4s
[CV 5/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.631) total time=   0.4s
[CV 6/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.623, test=0.633) total time=   0.4s
[CV 7/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.627, test=0.624) total time=   0.4s
[CV 8/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.614) total time=   0.4s
[CV 9/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.626, test=0.619) total time=   0.3s
[CV 10/10] END max_depth=4, max_features=3, min_samples_leaf=2000;, score=(train=0.628, test=0.617) total time=   0.3s

GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=RandomForestClassifier(max_depth=6, random_state=42),
             param_grid={'max_depth': [2, 3, 4], 'max_features': [2, 3],
                         'min_samples_leaf': [500, 1000, 2000]},
             return_train_score=True, scoring='roc_auc', verbose=4)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Model Performance

report_GridSearchCV_results(grid_search_rf)

- Best combination of hyperparams:
 {'max_depth': 4, 'max_features': 3, 'min_samples_leaf': 500} 

- Best mean_test_score:
 0.6409978359812152 

- Score by fold for best estimator:
 [0.6482674781776252, 0.6513156041355608, 0.6480194987161607, 0.6160162023648865, 0.6545420509716655, 0.6511617618076586, 0.6392177577137725, 0.619981345507844, 0.6313241923611761, 0.650132468055802] 

- Top 10 hyperparameter combinations by mean_test_score:

	mean_test_score	param_max_depth	param_min_samples_leaf	param_max_features
rank_test_score
1	0.640998	4	500	3
2	0.640955	2	500	3
3	0.640234	3	500	3
4	0.639044	4	500	2
5	0.638614	2	1000	3
6	0.638317	4	1000	3
7	0.637706	3	500	2
8	0.637399	3	1000	3
9	0.634454	2	500	2
10	0.634365	2	1000	2

compare_performance(grid_search_rf)

	train_AUC	val_AUC
1	0.640278	0.634454
2	0.639069	0.634365
3	0.623916	0.620221
4	0.646015	0.640955
5	0.643191	0.638614
6	0.625180	0.621237
7	0.643744	0.637706
8	0.639416	0.634266
9	0.623525	0.619434
10	0.646405	0.640234
Mean	0.637074	0.632149

best_model_rf=grid_search_rf.best_estimator_

plot_probability_std(best_model_rf, df_train, y_name, x_name, kf10, "Bagged Random Forest")

C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)

plot_avg_feature_importance(best_model_rf, df_train, y_name, x_name, kf10, "Pre-pruned Classification Tree")

C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)
C:\Users\woowe\anaconda\Lib\site-packages\sklearn\base.py:1473: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples,), for example using ravel().
  return fit_method(estimator, *args, **kwargs)

evaluate_model(best_model_rf, df_X_test_scaled, df_y_test)

Test AUC: 0.64
Accuracy: 0.59
Confusion Matrix:
[[2344  314]
 [1747  595]]

Classification Report:
              precision    recall  f1-score   support

           0       0.57      0.88      0.69      2658
           1       0.65      0.25      0.37      2342

    accuracy                           0.59      5000
   macro avg       0.61      0.57      0.53      5000
weighted avg       0.61      0.59      0.54      5000

plot_roc_curve(best_model_rf, df_X_test_scaled, df_y_test)

XGBoost (With regularization)

# Initialize model
xgb_model = xgb.XGBClassifier(random_state = 42)

# Define the hyperparameter grid
xgb_param_grid = {
    'colsample_bytree': [0.3, 0.7],
    'n_estimators': [50, 100, 200],
    'max_depth': [2, 5, 10],
    'alpha': [0, 0.1, 1], # Alpha/lasso regularisation
    'lambda': [0, 0.1, 1], # Lambda/ridge regularisation
    'learning_rate': [0.01, 0.05]    
}

# Create a GridSearchCV object
grid_search_xgb = GridSearchCV(param_grid=xgb_param_grid, estimator=xgb_model, 
                        scoring='roc_auc', cv=kf10, verbose=4, return_train_score=True)

# Fit the GridSearchCV object to the training data
grid_search_xgb.fit(df_train[x_name], df_train[y_name])

Fitting 10 folds for each of 324 candidates, totalling 3240 fits
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.660) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.628) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.664) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.653) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.636) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.641) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.646) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.657) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.655) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.655) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.651) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.643) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.636) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.659) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.660) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.662) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.625) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.659) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.658) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.656) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.636) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.663) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.662) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.664) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.628) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.659) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.658) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.656) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.646) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.853, test=0.642) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.659) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.654) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.665) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.648) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.644) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.634) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.663) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.661) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.640) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.656) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.663) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.667) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.639) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.663) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.661) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.660) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.650) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.659) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.648) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.657) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.613) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.653) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.643) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.857, test=0.632) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.652) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.653) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.655) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.896, test=0.614) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.662) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.651) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.645) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.646) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.632) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.652) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.941, test=0.646) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.648) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.650) total time=   0.9s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.943, test=0.610) total time=   0.7s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.656) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.645) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.947, test=0.641) total time=   0.8s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.643) total time=   0.9s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.632) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.650) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.645) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.629) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.664) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.652) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.636) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.667) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.655) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.646) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.655) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.660) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.660) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.621) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.655) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.651) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.635) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.807, test=0.655) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.660) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.661) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.662) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.829, test=0.624) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.658) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.655) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.645) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.635) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.656) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.663) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.662) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.664) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.627) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.660) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.659) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.656) total time=   0.8s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.645) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.641) total time=   0.9s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.659) total time=   0.9s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.654) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.651) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.665) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.663) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.663) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.661) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.640) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.663) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.662) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.666) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.638) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.661) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.661) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.651) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.660) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.652) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.654) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.844, test=0.655) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.616) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.653) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.642) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.632) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.652) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.654) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.652) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.654) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.616) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.654) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.644) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.648) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.635) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.656) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.648) total time=   0.9s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.649) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.935, test=0.649) total time=   0.8s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.613) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.946, test=0.654) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.648) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.946, test=0.640) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.643) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.632) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.653) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.668) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.635) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.666) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.646) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.657) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.658) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.659) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.623) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.654) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.639) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.791, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.661) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.659) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.663) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.625) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.658) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.656) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.646) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.810, test=0.637) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.658) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.664) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.661) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.664) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.630) total time=   0.9s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.660) total time=   1.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.660) total time=   1.0s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.833, test=0.657) total time=   1.0s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.647) total time=   1.4s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.643) total time=   1.4s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.827, test=0.659) total time=   1.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.658) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.665) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.664) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.659) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.642) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.646) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.657) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.663) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.661) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.666) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.638) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.661) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.660) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.650) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.658) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.660) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.658) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.834, test=0.620) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.653) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.831, test=0.646) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.835, test=0.639) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.653) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.871, test=0.656) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.869, test=0.656) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.866, test=0.654) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.624) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.874, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.656) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.648) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.869, test=0.648) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.637) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.869, test=0.656) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.654) total time=   0.9s
[CV 2/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.914, test=0.648) total time=   0.9s
[CV 3/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.650) total time=   0.8s
[CV 4/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.621) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.922, test=0.655) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.922, test=0.653) total time=   0.9s
[CV 7/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.926, test=0.645) total time=   0.9s
[CV 8/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.649) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.637) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.654) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.645) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.631) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.656) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.629) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.650) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.634) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.642) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.658) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.654) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.652) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.823, test=0.611) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.651) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.639) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.633) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.631) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.659) total time=   0.6s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.651) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.653) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.616) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.651) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.651) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.642) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.636) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.631) total time=   0.6s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.661) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.652) total time=   1.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.656) total time=   1.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.618) total time=   1.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.648) total time=   1.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.650) total time=   1.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.643) total time=   1.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.637) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.631) total time=   1.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.654) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.651) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.643) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.653) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.633) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.659) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.635) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.637) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.634) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.651) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.651) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.663) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.666) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.634) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.658) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.651) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.651) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.645) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.654) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.653) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.646) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.612) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.650) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.650) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.872, test=0.643) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.865, test=0.637) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.631) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.648) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.653) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.648) total time=   0.4s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.609) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.645) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.646) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.641) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.638) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.917, test=0.629) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.652) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.645) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.644) total time=   0.7s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.648) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.603) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.639) total time=   0.8s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.638) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.638) total time=   0.7s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.640) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.627) total time=   0.9s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.650) total time=   0.8s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.629) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.642) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.659) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.653) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.650) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.652) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.639) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.634) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.633) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.661) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.651) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.652) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.616) total time=   0.5s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.652) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.651) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.641) total time=   0.6s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.636) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.631) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.654) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.662) total time=   1.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.651) total time=   1.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.655) total time=   1.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.618) total time=   1.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.648) total time=   0.9s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.650) total time=   1.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.641) total time=   1.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.637) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.631) total time=   1.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.653) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.662) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.668) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.667) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.641) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.710, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.669) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.633) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.655) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.646) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.644) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.655) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.646) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.864, test=0.650) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.615) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.648) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.648) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.643) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.861, test=0.636) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.633) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.860, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.913, test=0.650) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.641) total time=   0.5s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.647) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.612) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.643) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.641) total time=   0.4s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.640) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.636) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.634) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.652) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.645) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.638) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.643) total time=   0.9s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.606) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.639) total time=   1.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.636) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.638) total time=   0.8s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.632) total time=   0.8s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.628) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.649) total time=   0.9s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.619) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.650) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.651) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.662) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.648) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.661) total time=   0.4s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.655) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.628) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.5s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.641) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.650) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.655) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.654) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.654) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.656) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.639) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.807, test=0.633) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.636) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.662) total time=   0.5s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.653) total time=   0.6s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.816, test=0.655) total time=   0.6s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.618) total time=   0.6s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.653) total time=   0.6s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.656) total time=   0.6s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.636) total time=   0.5s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.635) total time=   0.5s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.654) total time=   0.6s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.663) total time=   1.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.653) total time=   1.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.839, test=0.659) total time=   1.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.620) total time=   1.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.652) total time=   1.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.654) total time=   1.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.642) total time=   1.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.839, test=0.637) total time=   1.0s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.633) total time=   1.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.654) total time=   1.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.0s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time=   0.0s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.656) total time=   0.1s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.662) total time=   0.1s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.661) total time=   0.2s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.667) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.642) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.653) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.664) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.669) total time=   0.3s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.634) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.644) total time=   0.3s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.647) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.657) total time=   0.3s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.650) total time=   0.3s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.847, test=0.650) total time=   0.2s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.619) total time=   0.3s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.649) total time=   0.3s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.647) total time=   0.3s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.650) total time=   0.2s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.845, test=0.639) total time=   0.2s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.635) total time=   0.3s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.654) total time=   0.2s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.653) total time=   0.7s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.651) total time=   0.4s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.651) total time=   0.5s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.619) total time=   0.4s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.651) total time=   0.4s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.643) total time=   0.5s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.650) total time=   0.4s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.642) total time=   0.4s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.636) total time=   0.4s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.647) total time=   0.8s
[CV 2/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.648) total time=   0.8s
[CV 3/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.932, test=0.647) total time=   0.7s
[CV 4/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.616) total time=   0.8s
[CV 5/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.645) total time=   0.7s
[CV 6/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.640) total time=   0.8s
[CV 7/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.945, test=0.646) total time=   0.9s
[CV 8/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.640) total time=   0.7s
[CV 9/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.633) total time=   0.8s
[CV 10/10] END alpha=0, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.655) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.654) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.662) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.693, test=0.635) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.666) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.646) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.657) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.655) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.660) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.660) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.621) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.654) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.636) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.659) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.661) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.662) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.624) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.659) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.657) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.831, test=0.656) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.645) total time=   0.7s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.635) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.663) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.662) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.664) total time=   0.9s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.628) total time=   0.9s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.659) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.658) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.852, test=0.657) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.646) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.851, test=0.641) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.659) total time=   0.9s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.654) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.658) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.665) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.692, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.655) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.666) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.634) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.664) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.660) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.641) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.660) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.666) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.641) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.659) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.659) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.651) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.659) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.652) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.845, test=0.652) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.618) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.652) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.642) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.648) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.634) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.648) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.652) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.653) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.653) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.618) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.659) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.652) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.642) total time=   0.6s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.648) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.634) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.653) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.941, test=0.646) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.647) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.647) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.942, test=0.616) total time=   0.9s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.948, test=0.656) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.644) total time=   1.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.949, test=0.636) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.943, test=0.645) total time=   0.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.630) total time=   1.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.653) total time=   1.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.655) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.648) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.654) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.625) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.681, test=0.637) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.660) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.661) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.686, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.662) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.639) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.662) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.668) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.692, test=0.635) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.661) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.666) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.654) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.646) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.654) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.812, test=0.661) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.659) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.811, test=0.622) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.654) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.652) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.644) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.637) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.827, test=0.659) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.661) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.661) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.624) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.660) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.658) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.656) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.644) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.825, test=0.635) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.658) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.663) total time=   1.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.662) total time=   1.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.663) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.628) total time=   1.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.658) total time=   1.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.658) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.850, test=0.656) total time=   1.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.645) total time=   0.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.641) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.660) total time=   0.9s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.665) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.691, test=0.629) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.656) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.690, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.661) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.663) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.634) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.663) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.661) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.640) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.645) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.663) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.661) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.667) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.638) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.661) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.660) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.661) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.726, test=0.644) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.725, test=0.650) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.727, test=0.660) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.847, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.652) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.620) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.658) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.650) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.645) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.848, test=0.647) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.636) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.653) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.890, test=0.655) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.652) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.883, test=0.652) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.622) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.656) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.893, test=0.650) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.645) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.889, test=0.651) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.634) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.655) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.645) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.647) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.933, test=0.645) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.619) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.943, test=0.654) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.646) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.642) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.650) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.633) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.654) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.654) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.628) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.661) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.685, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.663) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.639) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.663) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.667) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.691, test=0.635) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.666) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.641) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.646) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.657) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.659) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.789, test=0.659) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.626) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.793, test=0.661) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.794, test=0.655) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.655) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.794, test=0.648) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.794, test=0.638) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.657) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.662) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.660) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.663) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.809, test=0.627) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.807, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.659) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.811, test=0.656) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.648) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.637) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.803, test=0.658) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.663) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.661) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.825, test=0.664) total time=   0.9s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.632) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.660) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.827, test=0.660) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.657) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.826, test=0.648) total time=   0.9s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.643) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.824, test=0.660) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.658) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.654) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.661) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.656) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.643) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.654) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.640) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.655) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.661) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.663) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.634) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.664) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.663) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.659) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.642) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.646) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.656) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.664) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.662) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.719, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.639) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.662) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.662) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.658) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.647) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.649) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.657) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.827, test=0.659) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.657) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.621) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.830, test=0.650) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.654) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.829, test=0.643) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.639) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.828, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.870, test=0.658) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.866, test=0.658) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.655) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.871, test=0.625) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.871, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.870, test=0.651) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.650) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.868, test=0.646) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.872, test=0.639) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.867, test=0.655) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.656) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.651) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.651) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.620) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.920, test=0.657) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.650) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.925, test=0.646) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.647) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.638) total time=   0.9s
[CV 10/10] END alpha=0.1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.655) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.633) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.665) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.629) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.634) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.641) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.651) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.651) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.823, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.651) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.638) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.633) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.632) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.655) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.661) total time=   0.7s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.651) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.828, test=0.652) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.617) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.652) total time=   0.7s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.651) total time=   0.7s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.635) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.835, test=0.631) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.832, test=0.655) total time=   0.7s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.662) total time=   1.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.650) total time=   1.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.856, test=0.656) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.619) total time=   1.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.861, test=0.648) total time=   1.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.649) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.865, test=0.643) total time=   1.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.858, test=0.636) total time=   1.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.863, test=0.631) total time=   1.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.654) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.650) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.661) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.655) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.648) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.631) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.662) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.662) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.666) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.636) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.652) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.641) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.643) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.652) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.663) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.666) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.640) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.659) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.652) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.645) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.643) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.653) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.654) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.649) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.646) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.612) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.651) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.645) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.876, test=0.646) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.631) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.868, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.649) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.920, test=0.647) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.915, test=0.647) total time=   0.6s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.610) total time=   0.7s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.910, test=0.649) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.641) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.921, test=0.644) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.638) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.920, test=0.633) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.909, test=0.647) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.963, test=0.644) total time=   1.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.965, test=0.640) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.639) total time=   0.9s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.603) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.956, test=0.643) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.637) total time=   0.9s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.968, test=0.639) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.636) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.966, test=0.633) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.643) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.619) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.660) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.649) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.651) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.661) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.672, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.655) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.652) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.662) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.677, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.657) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.649) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.633) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.661) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.656) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.628) total time=   0.4s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.649) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.635) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.684, test=0.641) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.651) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.660) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.650) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.652) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.613) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.817, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.651) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.638) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.633) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.632) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.813, test=0.657) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.660) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.651) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.653) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.617) total time=   0.6s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.652) total time=   0.6s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.653) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.836, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.636) total time=   0.6s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.833, test=0.631) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.830, test=0.656) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.661) total time=   1.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.857, test=0.651) total time=   1.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.854, test=0.656) total time=   1.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.619) total time=   1.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.859, test=0.649) total time=   1.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.651) total time=   1.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.862, test=0.642) total time=   1.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.636) total time=   1.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.860, test=0.632) total time=   1.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.855, test=0.655) total time=   1.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.649) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.651) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.644) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.653) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.662) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.664) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.633) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.659) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.659) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.629) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.661) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.636) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.642) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.662) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.667) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.632) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.660) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.667) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.709, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.641) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.708, test=0.645) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.707, test=0.654) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.664) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.664) total time=   0.4s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.667) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.742, test=0.637) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.661) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.664) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.654) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.739, test=0.647) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.740, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.871, test=0.653) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.866, test=0.652) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.647) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.614) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.869, test=0.649) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.870, test=0.645) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.873, test=0.640) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.863, test=0.638) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.867, test=0.634) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.862, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.918, test=0.650) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.919, test=0.644) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.645) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.614) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.646) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.914, test=0.640) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.916, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.642) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.911, test=0.636) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.648) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.644) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.964, test=0.639) total time=   0.9s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.645) total time=   0.7s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.609) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.643) total time=   0.9s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.632) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.637) total time=   0.8s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.641) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.634) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.645) total time=   0.8s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.646) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.622) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.650) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.654) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.647) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.639) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.647) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.662) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.660) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.655) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.665) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.629) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.648) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.641) total time=   0.4s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.650) total time=   0.4s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.654) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.798, test=0.654) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.810, test=0.615) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.653) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.656) total time=   0.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.809, test=0.640) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.637) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.652) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.816, test=0.661) total time=   0.6s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.653) total time=   0.6s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.655) total time=   0.5s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.826, test=0.618) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.652) total time=   0.5s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.654) total time=   0.5s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.823, test=0.642) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.635) total time=   0.5s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.635) total time=   0.6s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.654) total time=   0.6s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.662) total time=   0.9s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.653) total time=   1.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.838, test=0.658) total time=   1.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.620) total time=   1.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.653) total time=   1.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.654) total time=   1.3s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.643) total time=   1.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.838, test=0.637) total time=   1.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.635) total time=   1.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.841, test=0.655) total time=   1.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.654) total time=   0.0s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.624) total time=   0.0s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.657) total time=   0.0s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.0s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.640) total time=   0.0s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.626) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.2s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.631) total time=   0.2s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.660) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.666) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.653) total time=   0.2s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.634) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.659) total time=   0.1s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.657) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.663) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.632) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.663) total time=   0.2s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.650) total time=   0.1s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.638) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.641) total time=   0.1s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.650) total time=   0.1s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.661) total time=   0.2s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.660) total time=   0.1s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.667) total time=   0.1s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.632) total time=   0.1s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.662) total time=   0.1s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.666) total time=   0.1s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.652) total time=   0.4s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.642) total time=   0.1s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.645) total time=   0.2s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.656) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.662) total time=   0.4s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.659) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.733, test=0.666) total time=   0.3s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.633) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.662) total time=   0.3s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.663) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.653) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.646) total time=   0.3s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.729, test=0.647) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.658) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.658) total time=   0.3s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.846, test=0.656) total time=   0.3s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.844, test=0.654) total time=   0.2s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.618) total time=   0.3s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.652) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.651) total time=   0.2s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.647) total time=   0.3s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.845, test=0.640) total time=   0.2s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.634) total time=   0.3s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.649) total time=   0.3s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.655) total time=   0.5s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.887, test=0.651) total time=   0.5s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.652) total time=   0.4s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.886, test=0.615) total time=   0.5s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.651) total time=   0.4s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.891, test=0.646) total time=   0.4s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.646) total time=   0.5s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.882, test=0.644) total time=   0.4s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.892, test=0.634) total time=   0.5s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.888, test=0.649) total time=   0.5s
[CV 1/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.651) total time=   0.8s
[CV 2/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.648) total time=   0.8s
[CV 3/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.646) total time=   0.8s
[CV 4/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.937, test=0.615) total time=   0.8s
[CV 5/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.647) total time=   0.8s
[CV 6/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.640) total time=   0.8s
[CV 7/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.644) total time=   0.9s
[CV 8/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.938, test=0.645) total time=   0.8s
[CV 9/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.634) total time=   0.8s
[CV 10/10] END alpha=0.1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.650) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.622) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.659) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.638) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.653) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.630) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.639) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.643) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.654) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.667) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.635) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.662) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.666) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.655) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.640) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.662) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.781, test=0.662) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.625) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.658) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.787, test=0.656) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.646) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.639) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.780, test=0.658) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.661) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.661) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.663) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.800, test=0.628) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.798, test=0.659) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.660) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.801, test=0.656) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.646) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.798, test=0.636) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.795, test=0.659) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.819, test=0.662) total time=   0.9s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.662) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.665) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.820, test=0.632) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.660) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.661) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.820, test=0.656) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.817, test=0.645) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.819, test=0.642) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.660) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.658) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.644) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.661, test=0.663) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.656) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.660) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.629) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.660) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.653) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.639) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.655) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.663) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.664) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.634) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.664) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.657) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.643) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.646) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.656) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.663) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.662) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.664) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.639) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.662) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.664) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.723, test=0.659) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.649) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.658) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.658) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.658) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.816, test=0.660) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.623) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.653) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.650) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.821, test=0.642) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.825, test=0.637) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.656) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.860, test=0.658) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.861, test=0.656) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.857, test=0.657) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.626) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.658) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.654) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.864, test=0.648) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.859, test=0.645) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.865, test=0.636) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.658) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.917, test=0.651) total time=   0.9s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.650) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.913, test=0.652) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.622) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.656) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.919, test=0.649) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.918, test=0.647) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.912, test=0.645) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.635) total time=   0.8s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.913, test=0.658) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.622) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.655) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.659) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.680, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.653) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.684, test=0.631) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.665) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.654) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.639) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.643) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.654) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.663) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.664) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.667) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.690, test=0.635) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.662) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.666) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.655) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.641) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.646) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.657) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.782, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.661) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.779, test=0.661) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.625) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.782, test=0.661) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.783, test=0.659) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.656) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.785, test=0.645) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.784, test=0.639) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.778, test=0.659) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.660) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.797, test=0.661) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.794, test=0.662) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.797, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.660) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.796, test=0.662) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.655) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.797, test=0.646) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.795, test=0.636) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.793, test=0.659) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.663) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.662) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.813, test=0.665) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.817, test=0.632) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.660) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.662) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.818, test=0.656) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.815, test=0.646) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.816, test=0.642) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.812, test=0.660) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.658) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.645) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.659) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.629) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.661, test=0.663) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.657) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.659) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.651) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.648) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.661) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.664) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.689, test=0.630) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.660) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.653) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.639) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.655) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.664) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.634) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.662) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.657) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.643) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.656) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.662) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.663) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.667) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.724, test=0.638) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.719, test=0.662) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.664) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.722, test=0.658) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.721, test=0.650) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.658) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.658) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.658) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.814, test=0.659) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.818, test=0.660) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.820, test=0.655) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.648) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.819, test=0.643) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.822, test=0.636) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.815, test=0.657) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.857, test=0.657) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.858, test=0.656) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.854, test=0.659) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.861, test=0.625) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.860, test=0.658) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.860, test=0.654) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.862, test=0.647) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.856, test=0.644) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.863, test=0.634) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.855, test=0.659) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.913, test=0.652) total time=   0.9s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.911, test=0.652) total time=   0.8s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.655) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.621) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.654) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.916, test=0.650) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.915, test=0.644) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.910, test=0.645) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.914, test=0.634) total time=   0.8s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.909, test=0.658) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.656) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.651, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.652) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.660) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.623) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.642) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.644) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.653) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.621) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.649, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.644) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.652, test=0.623) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.651, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.654) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.655) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.655, test=0.624) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.656) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.651, test=0.662) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.652, test=0.644) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.654, test=0.626) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.644) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.653, test=0.647) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.660) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.675, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.679, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.675, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.676, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.678, test=0.638) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.677, test=0.652) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.662) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.679, test=0.665) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.683, test=0.630) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.663) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.665) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.653) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.682, test=0.639) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.681, test=0.643) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.680, test=0.654) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.663) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.663) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.685, test=0.667) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.689, test=0.635) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.663) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.666) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.654) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.688, test=0.641) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.687, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.686, test=0.656) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.767, test=0.659) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.769, test=0.658) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.764, test=0.659) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.769, test=0.627) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.769, test=0.661) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.768, test=0.659) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.770, test=0.654) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.770, test=0.644) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.768, test=0.640) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.765, test=0.658) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.780, test=0.661) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.660) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.778, test=0.662) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.629) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.660) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.662) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.783, test=0.654) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.781, test=0.645) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.780, test=0.638) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.778, test=0.660) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.663) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.800, test=0.662) total time=   1.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.795, test=0.664) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.800, test=0.634) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.661) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.663) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.800, test=0.656) total time=   0.8s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.646) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.799, test=0.643) total time=   1.0s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.796, test=0.661) total time=   1.0s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.655) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.654) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.624) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.650, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.645) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.654, test=0.625) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.643) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.652, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.661) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.655) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.658) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.658, test=0.628) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.648) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.664) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.657) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.663) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.633) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.659) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.662, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.634) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.659) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.661) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.665) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.688, test=0.631) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.660) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.654) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.639) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.644) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.656) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.664) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.696, test=0.666) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.636) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.664) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.658) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.698, test=0.644) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.646) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.697, test=0.657) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.663) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.662) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.666) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.720, test=0.640) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.715, test=0.662) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.660) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.648) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.718, test=0.650) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.717, test=0.660) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.659) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.657) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.796, test=0.661) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.625) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.664) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.655) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.806, test=0.650) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.643) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.637) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.800, test=0.655) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.657) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.838, test=0.655) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.834, test=0.660) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.628) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.841, test=0.664) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.840, test=0.657) total time=   0.7s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.843, test=0.649) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.839, test=0.647) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.844, test=0.637) total time=   0.4s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.837, test=0.658) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.892, test=0.653) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.892, test=0.653) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.890, test=0.657) total time=   0.8s
[CV 4/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.898, test=0.627) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.894, test=0.659) total time=   0.8s
[CV 6/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.894, test=0.652) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.897, test=0.645) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.893, test=0.648) total time=   0.8s
[CV 9/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.895, test=0.637) total time=   0.8s
[CV 10/10] END alpha=1, colsample_bytree=0.3, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.892, test=0.659) total time=   0.8s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.645) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.656) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.621) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.650) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.655) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.661) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.660) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.654) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.664) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.648) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.641) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.651) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.660) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.657) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.797, test=0.653) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.808, test=0.617) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.653) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.653) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.807, test=0.641) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.637) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.803, test=0.634) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.655) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.660) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.654) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.655) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.824, test=0.619) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.653) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.821, test=0.654) total time=   0.8s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.642) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.638) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.634) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.655) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.661) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.654) total time=   1.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.656) total time=   1.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.620) total time=   1.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.651) total time=   1.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.654) total time=   1.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.849, test=0.644) total time=   1.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.637) total time=   1.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.848, test=0.635) total time=   1.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.655) total time=   1.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.655) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.623) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.663) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.626) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.662) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.657) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.663) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.667) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.667, test=0.636) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.657) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.663) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.687, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.661) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.650) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.662) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.666) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.666) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.644) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.645) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.655) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.663) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.663) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.665) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.634) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.658) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.662) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.738, test=0.655) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.648) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.647) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.656) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.653) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.651) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.653) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.618) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.647) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.857, test=0.644) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.851, test=0.639) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.631) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.650) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.908, test=0.652) total time=   0.6s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.650) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.654) total time=   0.6s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.900, test=0.615) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.647) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.903, test=0.642) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.907, test=0.647) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.642) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.906, test=0.632) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.900, test=0.653) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.647) total time=   1.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.957, test=0.645) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.647) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.610) total time=   0.9s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.643) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.636) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.961, test=0.637) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.645) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.959, test=0.626) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.648) total time=   0.9s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.645) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.656) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.638) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.621) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.653) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.649, test=0.620) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.641) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.626) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.655) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.659) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.662) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.676, test=0.627) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.660) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.648) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.632) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.660) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.653) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.665) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.660) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.648) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.635) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.683, test=0.641) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.799, test=0.661) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.656) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.795, test=0.654) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.805, test=0.616) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.654) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.653) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.804, test=0.643) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.636) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.801, test=0.635) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.802, test=0.654) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.815, test=0.660) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.817, test=0.654) total time=   0.6s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.813, test=0.656) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.822, test=0.619) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.653) total time=   0.6s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.819, test=0.654) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.820, test=0.643) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.638) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.635) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.818, test=0.655) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.661) total time=   1.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.842, test=0.653) total time=   1.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.840, test=0.656) total time=   1.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.847, test=0.618) total time=   1.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.652) total time=   1.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.844, test=0.654) total time=   1.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.846, test=0.645) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.840, test=0.638) total time=   1.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.845, test=0.635) total time=   1.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.843, test=0.655) total time=   1.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.654) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.648) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.623) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.639) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.652) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.664) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.626) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.631) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.666) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.648) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.650) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.661) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.656) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.686, test=0.630) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.649) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.642) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.650) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.663) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.659) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.704, test=0.667) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.633) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.661) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.666) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.706, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.643) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.645) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.705, test=0.652) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.662) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.661) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.665) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.637) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.661) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.737, test=0.663) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.741, test=0.655) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.734, test=0.647) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.647) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.736, test=0.656) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.652) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.852, test=0.652) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.850, test=0.653) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.853, test=0.619) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.856, test=0.648) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.649) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.858, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.849, test=0.640) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.854, test=0.635) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.855, test=0.651) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.904, test=0.646) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.649) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.895, test=0.652) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.897, test=0.616) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.898, test=0.647) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.899, test=0.644) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.905, test=0.645) total time=   0.5s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.894, test=0.645) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.635) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.901, test=0.651) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.958, test=0.642) total time=   1.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.642) total time=   1.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.951, test=0.646) total time=   1.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.612) total time=   1.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.952, test=0.640) total time=   1.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.955, test=0.638) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.960, test=0.638) total time=   1.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.954, test=0.644) total time=   0.9s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.953, test=0.632) total time=   1.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=0.1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.962, test=0.646) total time=   1.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.651) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.643, test=0.651) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.645) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.619) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.648) total time=   0.0s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.656) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.640) total time=   0.0s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.620) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.642, test=0.639) total time=   0.0s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=50;, score=(train=0.644, test=0.637) total time=   0.0s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.652) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.644, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.642, test=0.644) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.618) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.653) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.643, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.642) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.647, test=0.620) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.640) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=100;, score=(train=0.645, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.654) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.648) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.647) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.621) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.654) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.645, test=0.657) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.645) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.648, test=0.620) total time=   0.4s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.647, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=2, n_estimators=200;, score=(train=0.646, test=0.641) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.658) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.668, test=0.649) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.666, test=0.660) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.671, test=0.627) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.659) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.670, test=0.631) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.669, test=0.639) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=50;, score=(train=0.667, test=0.647) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.660) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.650) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.670, test=0.662) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.675, test=0.628) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.658) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.671, test=0.660) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.649) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.673, test=0.632) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.674, test=0.640) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=100;, score=(train=0.672, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.678, test=0.660) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.654) total time=   0.4s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.678, test=0.665) total time=   0.4s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.682, test=0.628) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.678, test=0.661) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.679, test=0.663) total time=   0.4s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.648) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.636) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.681, test=0.640) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=5, n_estimators=200;, score=(train=0.680, test=0.651) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.787, test=0.662) total time=   0.4s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.786, test=0.656) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.781, test=0.660) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.791, test=0.619) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.655) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.789, test=0.655) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.791, test=0.644) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.789, test=0.635) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.637) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=50;, score=(train=0.788, test=0.655) total time=   0.4s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.801, test=0.661) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.655) total time=   0.7s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.799, test=0.659) total time=   0.7s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.808, test=0.621) total time=   0.6s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.803, test=0.656) total time=   0.7s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.805, test=0.656) total time=   0.6s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.806, test=0.644) total time=   0.7s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.803, test=0.637) total time=   0.6s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.804, test=0.637) total time=   0.7s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=100;, score=(train=0.802, test=0.657) total time=   0.5s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.826, test=0.662) total time=   1.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.654) total time=   1.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.823, test=0.659) total time=   1.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.622) total time=   1.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.829, test=0.653) total time=   1.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.828, test=0.656) total time=   1.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.831, test=0.645) total time=   1.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.823, test=0.638) total time=   1.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.830, test=0.635) total time=   1.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.01, max_depth=10, n_estimators=200;, score=(train=0.826, test=0.657) total time=   1.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.654) total time=   0.0s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.647) total time=   0.0s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.645, test=0.647) total time=   0.0s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.624) total time=   0.0s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.652) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.646, test=0.657) total time=   0.0s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.647, test=0.643) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.649, test=0.619) total time=   0.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.639) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=50;, score=(train=0.648, test=0.641) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.657) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.652) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.655) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.628) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.654, test=0.656) total time=   0.1s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.653, test=0.663) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.655, test=0.647) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.657, test=0.627) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.645) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=100;, score=(train=0.656, test=0.646) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.661) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.656) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.664) total time=   0.2s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.632) total time=   0.2s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.662) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.663, test=0.667) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.664, test=0.651) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.636) total time=   0.2s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.666, test=0.649) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=2, n_estimators=200;, score=(train=0.665, test=0.649) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.660) total time=   0.1s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.656) total time=   0.1s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.663) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.685, test=0.631) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.662) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.662) total time=   0.1s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.683, test=0.648) total time=   0.1s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.682, test=0.637) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.641) total time=   0.1s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=50;, score=(train=0.684, test=0.650) total time=   0.1s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.661) total time=   0.2s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.660) total time=   0.2s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.701, test=0.668) total time=   0.1s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.634) total time=   0.1s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.699, test=0.663) total time=   0.2s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.666) total time=   0.2s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.703, test=0.652) total time=   0.2s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.700, test=0.644) total time=   0.1s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.645) total time=   0.2s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=100;, score=(train=0.702, test=0.655) total time=   0.2s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.662) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.661) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.666) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.730, test=0.634) total time=   0.4s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.660) total time=   0.4s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.663) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.735, test=0.656) total time=   0.4s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.728, test=0.647) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.732, test=0.646) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=5, n_estimators=200;, score=(train=0.731, test=0.658) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.656) total time=   0.3s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.650) total time=   0.3s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.833, test=0.657) total time=   0.3s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.616) total time=   0.3s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.652) total time=   0.3s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.839, test=0.654) total time=   0.3s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.841, test=0.645) total time=   0.3s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.832, test=0.644) total time=   0.3s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.838, test=0.633) total time=   0.3s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=50;, score=(train=0.836, test=0.649) total time=   0.3s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.885, test=0.653) total time=   0.5s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.879, test=0.650) total time=   0.5s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.876, test=0.654) total time=   0.5s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.615) total time=   0.5s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.878, test=0.652) total time=   0.5s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.651) total time=   0.5s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.884, test=0.645) total time=   0.6s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.873, test=0.646) total time=   0.5s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.880, test=0.630) total time=   0.5s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=100;, score=(train=0.877, test=0.649) total time=   0.6s
[CV 1/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.940, test=0.650) total time=   0.8s
[CV 2/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.648) total time=   0.9s
[CV 3/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.647) total time=   0.9s
[CV 4/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.939, test=0.613) total time=   0.8s
[CV 5/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.928, test=0.647) total time=   0.9s
[CV 6/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.936, test=0.643) total time=   0.9s
[CV 7/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.944, test=0.635) total time=   0.9s
[CV 8/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.931, test=0.646) total time=   1.0s
[CV 9/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.934, test=0.629) total time=   0.9s
[CV 10/10] END alpha=1, colsample_bytree=0.7, lambda=1, learning_rate=0.05, max_depth=10, n_estimators=200;, score=(train=0.930, test=0.646) total time=   0.8s

C:\Users\woowe\anaconda\Lib\site-packages\numpy\ma\core.py:2820: RuntimeWarning: invalid value encountered in cast
  _data = np.array(data, dtype=dtype, copy=copy,

GridSearchCV(cv=KFold(n_splits=10, random_state=42, shuffle=True),
             estimator=XGBClassifier(base_score=None, booster=None,
                                     callbacks=None, colsample_bylevel=None,
                                     colsample_bynode=None,
                                     colsample_bytree=None, device=None,
                                     early_stopping_rounds=None,
                                     enable_categorical=False, eval_metric=None,
                                     feature_types=None, gamma=None,
                                     grow_policy=None, importance_type=None,
                                     in...
                                     max_leaves=None, min_child_weight=None,
                                     missing=nan, monotone_constraints=None,
                                     multi_strategy=None, n_estimators=None,
                                     n_jobs=None, num_parallel_tree=None,
                                     random_state=42, ...),
             param_grid={'alpha': [0, 0.1, 1], 'colsample_bytree': [0.3, 0.7],
                         'lambda': [0, 0.1, 1], 'learning_rate': [0.01, 0.05],
                         'max_depth': [2, 5, 10],
                         'n_estimators': [50, 100, 200]},
             return_train_score=True, scoring='roc_auc', verbose=4)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Model Performance

report_GridSearchCV_results(grid_search_xgb)

- Best combination of hyperparams:
 {'alpha': 1, 'colsample_bytree': 0.3, 'lambda': 1, 'learning_rate': 0.05, 'max_depth': 5, 'n_estimators': 200} 

- Best mean_test_score:
 0.6574395398994423 

- Score by fold for best estimator:
 [0.6630527191725473, 0.6624372683405841, 0.6663100724985688, 0.6403077003405951, 0.6622020538423473, 0.6628095450687967, 0.6601580040413113, 0.6475613409193599, 0.649832697452752, 0.6597239973175595] 

- Top 10 hyperparameter combinations by mean_test_score:

	mean_test_score	param_colsample_bytree	param_n_estimators	param_max_depth	param_alpha	param_lambda	param_learning_rate
rank_test_score
1	0.657440	0.3	200	5	1.0	1.0	0.05
2	0.656978	0.3	200	5	1.0	0.1	0.05
3	0.656880	0.3	200	5	1.0	0.0	0.05
4	0.656737	0.3	200	5	0.0	0.0	0.05
5	0.656657	0.3	200	5	0.0	0.1	0.05
6	0.656594	0.3	200	5	0.1	1.0	0.05
7	0.656527	0.3	200	5	0.0	1.0	0.05
8	0.656452	0.3	200	5	0.1	0.0	0.05
9	0.656324	0.3	200	5	0.1	0.1	0.05
10	0.655983	0.7	200	5	0.1	0.1	0.05

compare_performance(grid_search_xgb)

	train_AUC	val_AUC
1	0.648459	0.644285
2	0.650600	0.646056
3	0.652415	0.647117
4	0.679256	0.652434
5	0.684090	0.653665
6	0.690959	0.655600
7	0.813134	0.649548
8	0.829279	0.651748
9	0.851257	0.653650
10	0.651624	0.646783
Mean	0.715107	0.650089

best_model_xgb=grid_search_xgb.best_estimator_

plot_probability_std(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")

plot_avg_feature_importance(best_model_xgb, df_train, y_name, x_name, kf10, "XGBoost")

evaluate_model(best_model_xgb, df_X_test_scaled, df_y_test)

Test AUC: 0.66
Accuracy: 0.62
Confusion Matrix:
[[1984  674]
 [1216 1126]]

Classification Report:
              precision    recall  f1-score   support

           0       0.62      0.75      0.68      2658
           1       0.63      0.48      0.54      2342

    accuracy                           0.62      5000
   macro avg       0.62      0.61      0.61      5000
weighted avg       0.62      0.62      0.61      5000

plot_roc_curve(best_model_xgb, df_X_test_scaled, df_y_test)